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Abstract A large number of nodes, and significant system overhead are among the challenges. To tackle these problems, a new PBFT technique known as trace-PBFT (t-PBFT) is being contemplated for adoption in the food supply chain. The PBFT algorithm, short for Practical Byzantine Fault Tolerance, serves as the fundamental algorithm. The linkages throughout the supply chain may be categorized into three primary groups. As a response to the continuing discussion over the amount of data, the status of each node is continuously updated in real time. This metric is utilized to evaluate the dependability of the principal node and to analyze the overall performance of the nodes. To minimize the amount of data transmitted between nodes, we optimized the consensus process of the first algorithm. The distinct attributes of offering collaborative food technology were considered throughout this procedure. Experimental evidence indicates that the t-PBFT approach outperforms the PBFT algorithm in terms of throughput, request delay, and information overhead. An architectural paradigm is proposed by integrating the t-PBFT algorithm with a federated network. This model was developed in response to the distinct demands of the food supply chain. This model effectively captures data at each stage of the food supply chain to ensure information tracking while preserving the safety of the food technology flow process.

One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatly affected by how well the BCI works. At present, there are limitations on the accuracy of BCI recognition, the number of distinguishable command categories, and the execution duration of command recognition. Consequently, vehicles that are exclusively controlled by EEG signals demonstrate suboptimal control performance. To address the difficulty of improving the control capabilities of brain-controlled cars while maintaining BCI performance, a fuzzy logic-based technique called as Fuzzy Brain-Control Fusion Control is introduced. This approach uses Fuzzy Discrete Event System (FDES) supervisory theory to verify the accuracy of the driver's brain-controlled directives. Concurrently, a fuzzy logic-based automatic controller is developed to generate decisions automatically in accordance with the present state of the vehicle via fuzzy reasoning. The final decision is then reached through the application of secondary fuzzy reasoning to the accuracy of the driver's instructions and the automated decisions to make adjustments that are more consistent with human intent. A clever BCI gadget known as the Consistent State Visual Evoked Potential (SSVEP) is utilized to show the viability of the proposed technique. We recommend that additional research should be conducted at this time to confirm that our recommended system may further improve the control execution of BCI-fueled cars, regardless of whether BCIs have special limitations.

Objective In dermatological research, the focus on scalp and skin health has intensified, particularly regarding prevalent conditions like dandruff and erythema. This study aimed to utilize YOLOv7 model to develop an automated detection web-based system for these specific scalp lesions. Methods Utilizing a dataset of 2200 clinical images, the model's accuracy and robustness were assessed. The raw images were initially preprocessed by the Roboflow tool. We then trained and evaluated the YOLOv7 model, comparing its performance with several baseline models including YOLOv5, YOLOF, and the single-shot detector. Finally, the proposed model was integrated into a flask API-based web application using the flask-ngrok library. Results The YOLOv7 demonstrated exceptional performance, attaining a mean average precision of 98.6%, with precision and recall rates of 98.6% and 97.2%, respectively. When benchmarked against baseline models, the YOLOv7 demonstrated enhanced performance metrics both during the training phase and the testing process on unseen data. Conclusions This study not only validates the potential of YOLOv7 for scalp lesion diagnostic applications but also brings the integration of sophisticated AI models into practical healthcare solutions.

Most classification models for Alzheimer's Diagnosis (AD) do not have specific strategies for individual input samples, leading to the problem of easily overlooking personalized differences between samples. This research introduces a customized dynamically ensemble convolution neural network (PDECNN), which is able to build a specific integration strategy based on the distinctiveness of the sample. In this paper, we propose a personalized dynamic ensemble alzheimer's Diagnosis classification model. This model will dynamically modify the deteriorated brain areas of interest depending on various samples since it can adjust to variations in the degeneration of sample brain areas. In clinical problems, the PDECNN model has additional diagnostic importance since it can identify sample-specific degraded brain areas based on input samples. This model considers the variability of brain region degeneration levels between input samples, evaluates the degree of degeneration of specific brain regions using an attention mechanism, and selects and integrates brain region features based on the degree of degeneration. Furthermore, by redesigning the classification accuracy performance, we respectively improve it by 4 %, 11 %, and 8 %. Moreover, the degraded brain regions identified by the model show high consistency with the clinical manifestations of AD.

Objective This study aims to create a robust and interpretable method for predicting dementia in Parkinson's disease (PD), especially in resource-limited settings. The model aims to be accurate even with small datasets and missing values, ultimately promoting its use in clinical practice to benefit patients and medical professionals. Methods Our study introduces LightGBM–TabPFN, a novel hybrid model for predicting dementia conversion in PD. Combining LightGBM's strength in handling missing values with TabPFN's ability to exploit small datasets, LightGBM–TabPFN outperforms seven existing methods, achieving outstanding accuracy and interpretability thanks to SHAP analysis. This analysis leverages data from 242 PD patients across 17 variables. Results Our LightGBM–TabPFN model significantly outperformed seven existing methods. Achieving an accuracy of 0.9592 and an area under the ROC curve of 0.9737. Conclusions The interpretable LightGBM–TabPFN with SHAP signifies a significant advancement in predictive modeling for neurodegenerative diseases. This study not only improves dementia prediction in PD but also provides clinical professionals with insights into model predictions, offering opportunities for application in clinical settings.

Abstract In this paper, the new subclass $$\mathcal {S}^n_{b,\lambda ,\delta ,p} ({\alpha })$$ S b , λ , δ , p n ( α ) of a linear differential operator’s $$\mathcal {N}_{\lambda ,\delta ,p}^{n}f(\zeta )$$ N λ , δ , p n f ( ζ ) associated with multivalent analytical function has been introduced. Further, the coefficient inequalities, extreme points for the extremal function, sharpness of the growth and distortion bounds, partial sums, starlikeness, and convexity of the subclass is investigated.

Abstract Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS’s ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.

High-resolution pathological images play a pivotal role in accurate disease diagnosis and are important in precision medicine. However, obtaining real-time high-resolution images faces challenges due to hardware limitations and scanning time constraints. Conventional image super-resolution restoration algorithms struggle to provide satisfactory results for pathological images, resulting in blurred and unrealistic restorations. In response to this challenge, this research proposes a pioneering approach by integrating Internet of Things (IoT) technology with Local Attention Dual Network (IOT-LAT) for super-resolution restoration of pathological images. The enhanced IOT-LATincorporates IoT-based data acquisition and processing, IoT-Enhanced non-local attention mechanisms, Gaussian constraint, and parameter-sharing strategies in up-sampling and down-sampling branches. This integration enables real-time super-resolution restoration of pathological images with improved accuracy. The reconstructed images exhibit a structural similarity of 0.914 and a peak signal-to-noise ratio of 30.84 dB. These results validate the effectiveness of the suggested approach in precisely reconstructing high-frequency details and improving modelling efficiency via a lightweight non-local attention mechanism enhanced by the Internet of Things. This work discusses IoT and non-local attention dual networks to improve pathological image super-resolution restoration. This allows for faster and more accurate disease diagnosis and treatment in precision medicine.

Bioinformatics and Healthcare Integration Disease prediction models have been revolutionized by Big Data. These models, which make use of extensive medical data, predict illnesses before symptoms appear. Deep neural networks are well-known for their ability to increase accuracy by extending the network's depth and modifying weights through gradient descent. Traditional approaches, however, are hindered by issues such as gradient instability and delayed training. As a substitute, the Broad Learning (BL) system is introduced, which avoids gradient descent in favor of quick reconstruction by incremental learning. However, BL has trouble extracting complicated features from medical data, which makes it perform poorly in cases involving complex healthcare. We suggest ABL, which combines the effectiveness of BL with the noise reduction of Denoising Auto Encoder (AE), to address this. Robust feature extraction is an area in which the hybrid model shines, especially in intricate medical environments. Accuracy of up to 98.50 % is achieved by remarkable results from validation using a variety of datasets. The ability of ABL to quickly adapt through incremental learning suggests that it may be used to forecast diseases in complicated healthcare contexts with agility and accuracy.

Abstract A simplified mathematical model has been developed for understanding combined effects of surface roughness, viscosity variation and couple stresses on the squeeze film behaviour of a flat and a curved circular plate in the presence of transverse magnetic field. The Stokes (1966) couple stress fluid model is included to account for the couple stresses arising due to the presence of microstructure additives in the lubricant. In the context of Christensen’s (1969) stochastic theory for the lubrication of rough surfaces, two types of one-dimensional roughness patterns (radial and azimuthal) are considered. The governing modified stochastic Reynolds type equations are derived for these roughness patterns. Expressions for the mean squeeze film characteristics are obtained. Numerical computations of the results show that the azimuthal roughness pattern on the curved circular and flat plate results in more pressure buildup whereas performance of the squeeze film suffers due to the radial roughness pattern. Further the Lorentz force characterized by the Hartmann number, couple stress parameter and viscosity variation parameter improve the performance of the squeeze film lubrication as compared to the classical case (Non-magnetic, Newtonian case and non-viscous case).

According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) and a Mel-spectrogram are the basis of this cough recognition technique. First, we combine the voice in a variety of intricate settings and improve the audio data. After that, we preprocess the data to make sure its length is consistent and create a Mel-spectrogram out of it. A novel model for brain tumor segmentation (BTS), Intelligence Cascade U-Net (ICU-Net), is proposed to address these issues. It is built on dynamic convolution and uses a non-local attention mechanism. In order to reconstruct more detailed spatial information on brain tumours, the principal design is a two-stage cascade of 3DU-Net. The paper’s objective is to identify the best learnable parameters that will maximize the likelihood of the data. After the network’s ability to gather long-distance dependencies for AI, Expectation–Maximization is applied to the cascade network’s lateral connections, enabling it to leverage contextual data more effectively. Lastly, to enhance the network’s ability to capture local characteristics, dynamic convolutions with local adaptive capabilities are used in place of the cascade network’s standard convolutions. We compared our results to those of other typical methods and ran extensive testing utilising the publicly available BraTS 2019/2020 datasets. The suggested method performs well on tasks involving BTS, according to the experimental data. The Dice scores for tumor core (TC), complete tumor, and enhanced tumor segmentation BraTS 2019/2020 validation sets are 0.897/0.903, 0.826/0.828, and 0.781/0.786, respectively, indicating high performance in BTS.

In recent times, the research community has demonstrated significant interest in Underwater Wireless Sensor Networks (UWSNs), where extensive sensor deployments in oceans and rivers aim to monitor the underwater environment. Energy consumption poses a primary challenge due to the difficulty of replacing or recharging batteries in these environments. Existing studies have employed K-Means technology to minimize power consumption in underwater transmission nodes. However, these studies have often overlooked the consideration of residual energy and void region creation in their optimization approaches. To address these challenges, we introduce a novel Hybrid path finder-based vortex search (HPF-VS) algorithm, utilized for cluster head selection and optimization of node locations and remaining energy. To extend network coverage beyond limited transmission ranges, inaccessible nodes at the network periphery employ the improved Dwarf Mongoose Optimization (IDMO) algorithm. Our proposed techniques demonstrate superior performance compared to existing methods, showcasing minimized energy consumption, reduced delay, improved packet delivery ratio, and enhanced throughput. Specifically, the proposed approach achieves a delay of 2.01 s and a throughput of 32.21 Kbps, surpassing the performance of state-of-the-art methodologies we benchmarked against.

Abstract The intention of this work is to study a mathematical model for fractal-fractional tuberculosis and COVID-19 co-infection under the Atangana-Baleanu fractal-fractional operator. Firstly, we formulate the tuberculosis and COVID-19 co-infection model by considering the tuberculosis recovery individuals, the COVID-19 recovery individuals, and both disease recovery compartment in the proposed model. The fixed point approach is utilized to explore the existence and uniqueness of the solution in the suggested model. The stability analysis related to solve the Ulam-Hyers stability is also investigated. This paper is based on Lagrange’s interpolation polynomial in the numerical scheme, which is validated through a specific case with a comparative numerical analysis for different values of the fractional and fractal orders.

This study focuses on synthesizing nano-scaled Fe2O3 particles using Araucaria columnaris (Cook's pine) leaf extract under atmosphere circumstances. These extracts serve as both a reductant and a stabilizer. Fe2O3 nanoparticle synthesis was confirmed through a comprehensive characterization process using many analytical techniques. The analysis of XRD patterns indicated that the examined samples consisted of nanoparticles of γ-Fe2O3, exhibiting a cubic crystal structure with a high degree of crystallization. Furthermore, the calculated crystallite size was resolute to be 24.5 nm. The scanning electron microscopy pictures revealed that the created samples exhibited a combination of spherical and polyhedral morphologies. The investigation of optical characteristics, such as light absorption and bandgap width, was conducted using UV–visible spectroscopy, employing a bandgap energy value of 3.78 eV. The study aimed to assess the removal effectiveness of Eosin yellow, Malachite green, Reactive black, and Rhodamine 6G dyes using Fe2O3 nanoparticles synthesized as a photocatalyst which exhibited remarkable photocatalytic efficacy in the breakdown of dyes when exposed to sunlight, and the reaction period of 90 min resulted in a maximum degradation rate of 97.4 %. Considering the promising results, utilizing Fe2O3 nanoparticles as a photocatalyst for degrading dyes in wastewater is feasible. Additionally, Araucaria columnaris leaf extract can be an environmentally friendly and economically viable method for synthesizing Fe2O3 nanoparticles.

In order to meet the increasing need for reduced power consumption, reduced circuit size, and increased operating speed, this research focuses on the efficient design of reversible shift registers. The shift register is an essential building block of both arithmetic logic units (ALUs) and reversible memory circuits. In this research, an improved layout is shown for a variety of shift register types, including those with serial input and parallel output (SIPO), serial input and serial output (SISO), parallel input and parallel output (PIPO), parallel input and serial output (PISO), and universal shift registers (USR). Our approaches lessen waste in compared to standard procedures by maximizing '0′ GO and reducing CI. The 4-bit SIPO register has a 70 % increase in CI performance over previous designs and also provides SISO capability with a 50 % CI and 100 % GO boost. Our proposed designs for the SIPO, SISO, PIPO, and PISO registers also result in a 70 % and 80 % improvement in CI and GO for the 4-bit PISO register. We enhance the 4-bit Universal Shift Register (USR) design by 62.5 % in CI, 50 % in CI, 12.5 % in GO, and 56.25 in GO over the previous design.

Objective The Eastern Cooperative Oncology Group performance status (ECOG PS) is a widely recognized measure used to assess the functional abilities of cancer patients and predict their prognosis. It plays a crucial role in guiding treatment decisions made by physicians. This study aimed to build a stacking ensemble-based prognosis predictor model for predicting the ECOG PS of a liver cancer patient undergoing treatment. Methods We used Light Gradient Boosting Machine (LightGBM) as the meta-model, and five base models, including Random Forest (RF), Extra Trees (ET), AdaBoost (Ada), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). After preprocessing the data and applying feature selection method, the stacking ensemble model was trained using 1622 liver cancer patients’ data and 46 variables. We also integrated the stacking ensemble model with a LIME-based explainable model to obtain model prediction explainability. Results According to the research, the best combination of the stacking ensemble model is ET + XGBoost + RF + GBM + Ada + LightGBM and achieved a ROC AUC of 0.9826 on the training set and 0.9675 on the test set. Conclusions This explainable stacking ensemble model can become a helpful tool for objectively predicting ECOG PS in liver cancer patients and aiding healthcare practitioners to adapt their treatment approach more effectively.

Background: Many complex factors contribute to suicide in older adults. The suicidal ideation that precedes suicide is an especially direct predictor of suicide. This study aimed to identify the effects between variables affecting suicidal ideation among older adults using the International Classification of Functioning, Disability and Health (ICF) model and understand the causal relationships to systematize complex factors. Methods: This study used data from 9920 community-dwelling older adults who completed a national survey in 2020 to classify predictors of suicidal ideation (e.g., depression, subjective health status, sociodemographic factors, health factors, social support, instrumental activities of daily living (IADL), and social participation) by using the ICF model. To determine the causal relationship between variables, this study examined significance based on the critical ratio (C.R.) and squared multiple correlation (SMC) by using a path model. Results: Gender, education level, economic level, age, IADL, relationship satisfaction with a child, depression, and the number of chronic diseases significantly affected suicidal ideation, while age, employment status, participation in social groups, formal and informal support, satisfaction with a friend/neighbor relationship, and subjective health status did not significantly influence it. Moreover, depression mediated the relationship between each of these variables and suicidal ideation. Conclusions: It was found that depression was the most direct and mediating factor in suicidal ideation among many factors affecting the suicidal ideation of community-dwelling older adults. Additional studies should be conducted to develop community-level strategies based on these factors and understand causal relationships.

ObjectiveThis study identified major risk factors for depression in community diabetic patients using machine learning techniques and developed predictive models for predicting the high-risk group for depression in diabetic patients based on multiple risk factors.MethodsThis study analyzed 26,829 adults living in the community who were diagnosed with diabetes by a doctor. The prevalence of a depressive disorder was the dependent variable in this study. This study developed a model for predicting diabetic depression using multiple logistic regression, which corrected all confounding factors in order to identify the relationship (influence) of predictive factors for diabetic depression by entering the top nine variables with high importance, which were identified in CatBoost.ResultsThe prevalence of depression was 22.4% (n = 6,001). This study calculated the importance of factors related to depression in diabetic patients living in South Korean community using CatBoost to find that the top nine variables with high importance were gender, smoking status, changes in drinking before and after the COVID-19 pandemic, changes in smoking before and after the COVID-19 pandemic, subjective health, concern about economic loss due to the COVID-19 pandemic, changes in sleeping hours due to the COVID-19 pandemic, economic activity, and the number of people you can ask for help in a disaster situation such as COVID-19 infection.ConclusionIt is necessary to identify the high-risk group for diabetes and depression at an early stage, while considering multiple risk factors, and to seek a personalized psychological support system at the primary medical level, which can improve their mental health.

Classification problems due to data imbalance occur in many fields and have long been studied in the machine learning field. Many real-world datasets suffer from the issue of class imbalance, which occurs when the sizes of classes are not uniform; thus, data belonging to the minority class are likely to be misclassified. It is particularly important to overcome this issue when dealing with medical data because class imbalance inevitably arises due to incidence rates within medical datasets. This study adjusted the imbalance ratio (IR) within the National Biobank of Korea dataset “Epidemiologic data of Parkinson’s disease dementia patients” to values of 6.8 (raw data), 9, and 19 and compared four traditional oversampling methods with techniques using the conditional generative adversarial network (CGAN) and conditional tabular generative adversarial network (CTGAN). The results showed that when the classes were balanced with CGAN and CTGAN, they showed a better classification performance than the more traditional oversampling techniques based on the AUC and F1-score. We were able to expand the application scope of GAN, widely used in unstructured data, to structured data. We also offer a better solution for the imbalanced data problem and suggest future research directions.

ObjectiveThis study identified factors related to adolescent obesity during the COVID-19 pandemic by using machine learning techniques and developed a model for predicting high-risk obesity groups among South Korean adolescents based on the result.Materials and methodsThis study analyzed 50,858 subjects (male: 26,535 subjects, and female: 24,323 subjects) between 12 and 18 years old. Outcome variables were classified into two classes (normal or obesity) based on body mass index (BMI). The explanatory variables included demographic factors, mental health factors, life habit factors, exercise factors, and academic factors. This study developed a model for predicting adolescent obesity by using multiple logistic regressions that corrected all confounding factors to understand the relationship between predictors for South Korean adolescent obesity by inputting the seven variables with the highest Shapley values found in categorical boosting (CatBoost).ResultsIn this study, the top seven variables with a high impact on model output (based on SHAP values in CatBoost) were gender, mean sitting hours per day, the number of days of conducting strength training in the past seven days, academic performance, the number of days of drinking soda in the past seven days, the number of days of conducting the moderate-intensity physical activity for 60 min or more per day in the past seven days, and subjective stress perception level.ConclusionTo prevent obesity in adolescents, it is required to detect adolescents vulnerable to obesity early and conduct monitoring continuously to manage their physical health.

ObjectiveThis epidemiological study (1) identified factors associated with impaired fasting glucose using 3,019 subjects (≥30 years old and

Although the full vaccination rate of South Korea compared to other countries, concerns about the effectiveness of the vaccine are growing as new COVID variants such as Alpha, Beta, Gamma, Delta, and Omicron appear over time. In this study, we collected Twitter data in South Korea that contained keywords like vaccines after the outbreak of the Omicron variant from 27 November 2021 to 14 February 2022. First, we analyzed the relationship between potential keywords associated with vaccination after the appearance of the Omicron variant in Twitter using network analysis. Second, we developed an efficient model for predicting the emotion of speech regarding vaccination after the COVID-19 Omicron variant pandemic by using deep learning algorithms. We constructed sentiment analysis models regarding vaccination after the COVID-19 Omicron pandemic by using five algorithms [i.e., support vector machine (SVM), recurrent neural networks (RNNs), long short-term memory models (LSTMs), bidirectional encoder representations from transformers (BERT), and Korean BERT (KoBERT)]. The results confirmed that KoBERT showed the best performance (71%) in all predictive performance indicators (accuracy, precision, and F1 score). It is necessary to prepare measures to alleviate the negative factorss of the public about vaccination in the long-term pandemic situation and help the public recognize the efficacy and safety of vaccination by using big data based on the results of this study.

COVID-19 has further aggravated problems by compelling people to stay indoors and limit social interactions, leading to a worsening of the depression situation. This study aimed to construct a TabNet model combined with SHapley Additive exPlanations (SHAP) to predict depression in South Korean society during the COVID-19 pandemic. We used a tabular dataset extracted from the Seoul Welfare Survey with a total of 3027 samples. The TabNet model was trained on this dataset, and its performance was compared to that of several other machine learning models, including Random Forest, eXtreme Gradient Boosting, Light Gradient Boosting, and CatBoost. According to the results, the TabNet model achieved an Area under the receiver operating characteristic curve value (AUC) of 0.9957 on the training set and an AUC of 0.9937 on the test set. Additionally, the study investigated the TabNet model’s local interpretability using SHapley Additive exPlanations (SHAP) to provide post hoc global and local explanations for the proposed model. By combining the TabNet model with SHAP, our proposed model might offer a valuable tool for professionals in social fields, and psychologists without expert knowledge in the field of data analysis can easily comprehend the decision-making process of this AI model.

The Eastern Cooperative Oncology Group (ECOG) performance status is a widely used method for evaluating the functional abilities of cancer patients and predicting their prognosis. It is essential for healthcare providers to frequently assess the ECOG performance status of lung cancer patients to ensure that it accurately reflects their current functional abilities and to modify their treatment plan accordingly. This study aimed to develop and evaluate an AdaBoost classification (ADB-C) model to predict a lung cancer patient’s performance status following treatment. According to the results, the ADB-C model has the highest “Area under the receiver operating characteristic curve” (ROC AUC) score at 0.7890 which outperformed other benchmark models including Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest, XGBoost, and TabNet. In order to achieve model prediction explainability, we combined the ADB-C model with a LIME-based explainable model. This explainable ADB-C model may assist medical professionals in exploring effective cancer treatments that would not negatively impact the post-treatment performance status of a patient.

ObjectiveThis study developed a model to predict groups vulnerable to suicidal ideation after the declaration of the COVID-19 pandemic based on nomogram techniques targeting 54,948 adolescents who participated in a national survey in South Korea.MethodsThis study developed a model to predict suicidal ideation by using logistic regression analysis. The model aimed to understand the relationship between predictors associated with the suicidal ideation of South Korean adolescents by using the top seven variables with the highest feature importance confirmed in XGBoost (extreme gradient boosting). The regression model was developed using a nomogram so that medical workers could easily interpret the probability of suicidal ideation and identify groups vulnerable to suicidal ideation.ResultsThis epidemiological study predicted that eighth graders who experienced depression in the past 12 months, had a lot of subjective stress, frequently felt lonely in the last 12 months, experienced much-worsened household economic status during the COVID-19 pandemic, and had poor academic performance were vulnerable to suicidal ideation. The results of 10-fold cross-validation revealed that the area under the curve (AUC) of the adolescent suicidal ideation prediction nomogram was 0.86, general accuracy was 0.89, precision was 0.87, recall was 0.89, and the F1-score was 0.88.ConclusionIt is required to recognize the seriousness of adolescent suicide and mental health after the onset of the COVID-19 pandemic and prepare a customized support system that considers the characteristics of persons at risk of suicide at the school or community level.

ObjectiveThere are still not enough studies on the prediction of non-utilization of a complication test or a glycated hemoglobin test for preventing diabetes complications by using large-scale community-based big data. This study identified the ratio of not taking a diabetes complication test (fundus examination and microprotein urination test) among adult diabetic patients over 19 years using a national survey conducted in South Korea and developed a model for predicting the probability of not taking a diabetes complication test based on it.MethodsThis study analyzed 25,811 subjects who responded that they had been diagnosed with diabetes by a doctor in the 2020 Community Health Survey. Outcome variables were defined as the utilization of the microprotein urination test and the fundus examination during the past year. This study developed a model for predicting the utilization of a diabetes complication test using logistic regression analysis and nomogram to understand the relationship of predictive factors on the utilization of a diabetes complication test.ResultsThe results of this study confirmed that age, education level, the recognition of own blood glucose level, current diabetes treatment, diabetes management education, not conducting the glycated hemoglobin test in the past year, smoking, single-person household, subjectively good health, and living in the rural area were independently related to the non-utilization of diabetes complication test after the COVID-19 pandemic.ConclusionAdditional longitudinal studies are required to confirm the causality of the non-utilization of diabetes complication screening tests.

Since the emergence of the Coronavirus disease (COVID-19) pandemic, the disease has affected more than 675 million people worldwide, including more than 6.87 million deaths. To mitigate the effects of this pandemic, many countries established control measures to contain its spread. Their riposte was based on a combination of pharmaceutical (vaccination) and non-pharmaceutical (such as facemask wearing, social distancing, and quarantine) measures. In this way, cross-sectional research was conducted in Algeria from 23 December 2021 to 12 March 2022 to investigate the effectiveness of preventative interventions in lowering COVID-19 infection and severity. More specifically, we investigated the link between mask-wearing and infection on one side, and the relationship between vaccination and the risk of hospitalization on the other. For this purpose, we used binary logistic regression modeling that allows learning the role of mask-wearing and vaccination in a heterogeneous society with respect to compliance with barrier measures. This study determined that wearing a mask is equally important for people of all ages. Further, findings revealed that the risk of infection was 0.79 times lower among those who were using masks (odds ratio (OR) = 0.79; confidence interval (CI) 95% = 0.668–0.936; p-value = 0.006). At the same time, vaccination is a necessary preventive measure as the risk of hospitalization increases with age. Compared with those who did not get vaccinated, those who got vaccinated were 0.429 times less likely to end up in the hospital (OR = 0.429; CI95% = 0.273–0.676; p < 0.0001). The model performance demonstrates significant relationships between the dependent and independent variables, with the absence of over-dispersion in both studied models, such as the Akaike Information Criterion (AIC) scores. These findings emphasize the significance of preventative measures and immunization in the battle against the COVID-19 pandemic.

Survival after out-of-hospital cardiac arrest (OHCA) is contingent on time-sensitive interventions taken by onlookers, emergency call operators, first responders, emergency medical services (EMS) personnel, and hospital healthcare staff. By building integrated cardiac resuscitation systems of care, measurement systems, and techniques for assuring the correct execution of evidence-based treatments by bystanders, EMS professionals, and hospital employees, survival results can be improved. To aid in OHCA prognosis and treatment, we develop a hybrid agnostic explanation TabNet (HAE-TabNet) model to predict OHCA patient survival. According to the results, the HAE-TabNet model has an “Area under the receiver operating characteristic curve value” (ROC AUC) score of 0.9934 (95% confidence interval 0.9933–0.9935), which outperformed other machine learning models in the previous study, such as XGBoost, k-nearest neighbors, random forest, decision trees, and logistic regression. In order to achieve model prediction explainability for a non-expert in the artificial intelligence field, we combined the HAE-TabNet model with a LIME-based explainable model. This HAE-TabNet model may assist medical professionals in the prognosis and treatment of OHCA patients effectively.

This study aimed to test a predictive model for depression in older adults in the community after the COVID-19 pandemic and identify influencing factors using the International Classification of Functioning, Disability, and Health (ICF). The subjects of this study were 9920 older adults in South Korean local communities. The analysis results of path analysis and bootstrapping analysis revealed that subjective health status, instrumental activities of daily living (IADL), number of chronic diseases, social support satisfaction, household economic level, informal support, and participation in social groups were factors directly influencing depression, while formal support, age, gender, education level, employment status, and participation in social groups were factors indirectly affecting it. It will be needed to prepare measures to prevent depression in older adults during an infectious disease pandemic, such as the COVID-19 pandemic, based on the results of this study.

The Korea National Health and Nutrition Examination Survey (2020) reported that the prevalence of obesity (≥19 years old) was 31.4% in 2011, but it increased to 33.8% in 2019 and 38.3% in 2020, which confirmed that it increased rapidly after the outbreak of COVID-19. Obesity increases not only the risk of infection with COVID-19 but also severity and fatality rate after being infected with COVID-19 compared to people with normal weight or underweight. Therefore, identifying the difference in potential factors for obesity before and after the pandemic is an important issue in health science. This study identified the keywords and topics that were formed before and after the COVID-19 pandemic in the South Korean society and how they had been changing by conducting a web crawling of South Korea's news big data using “obesity” as a keyword. This study also developed models for predicting timing before and after the COVID-19 pandemic using keywords. Topic modeling results was found that the trend of keywords was different between before the COVID-19 pandemic and after the COVID-19 pandemic: topics such as “degenerative arthritis”, “diet,” and “side effects of diet treatment” were derived before the COVID-19 pandemic, while topics such as “COVID blues” and “relationship between dietary behavior and disease” were confirmed after the COVID-19 pandemic. This study also showed that both RNN and LSTM had high accuracy (over 97%), but the accuracy of the RNN model (98.22%) had higher than that of the LSTM model (97.12%) by 0.24%. Based on the results of this study, it will be necessary to continuously pay attention to the newly added obesity-related factors after the COVID-19 pandemic and to prepare countermeasures at the social level based on the results of this study.

This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a nomogram that could help primary physicians easily interpret high-risk groups of depressive disorders in primary care settings based on the major predictors derived from machine learning. This study analyzed 582 female older adults (≥60 years old) living alone. The depressive disorder, a target variable, was measured using the Korean version of Patient Health Questionnaire-9. This study developed five single predictive models (GBM, Random Forest, Adaboost, SVM, XGBoost) and six stacking ensemble models (GBM + Bayesian regression, RandomForest + Bayesian regression, Adaboost + Bayesian regression, SVM + Bayesian regression, XGBoost + Bayesian regression, GBM + RandomForest + Adaboost + SVM + XGBoost + Bayesian regression) to predict depressive disorders. The naive Bayesian nomogram confirmed that stress perception, subjective health, n-6 fatty acid, n-3 fatty acid, mean hours of sitting per day, and mean daily sleep hours were six major variables related to the depressive disorders of female older adults living alone. Based on the results of this study, it is required to evaluate the multiple risk factors for depression including various measurable factors such as social support.

Depression symptoms are comparable to Parkinson’s disease symptoms, including attention deficit, fatigue, and sleep disruption, as well as symptoms of dementia such as apathy. As a result, it is difficult for Parkinson’s disease caregivers to diagnose depression early. We examined a LIME-based stacking ensemble model to predict the depression of patients with Parkinson’s disease. This study used the epidemiologic data of Parkinson’s disease dementia patients (EPD) from the Korea Disease Control and Prevention Agency’s National Biobank, which included 526 patients’ information. We used Logistic Regression (LR) as the meta-model, and five base models, including LightGBM (LGBM), K-nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), and AdaBoost. After cleansing the data, the stacking ensemble model was trained using 261 participants’ data and 10 variables. According to the research, the best combination of the stacking ensemble model is ET + LGBM + RF + LR, a harmonious model. In order to achieve model prediction explainability, we also combined the stacking ensemble model with a LIME-based explainable model. This explainable stacking ensemble model can help identify the patients and start treatment on them early in a way that medical professionals can comprehend.

The impact of the COVID-19 epidemic on the mental health of elderly individuals is causing considerable worry. We examined a deep neural network (DNN) model to predict the depression of the elderly population during the pandemic period based on social factors related to stress, health status, daily changes, and physical distancing. This study used vast data from the 2020 Community Health Survey of the Republic of Korea, which included 97,230 people over the age of 60. After cleansing the data, the DNN model was trained using 36,258 participants’ data and 22 variables. We also integrated the DNN model with a LIME-based explainable model to achieve model prediction explainability. According to the research, the model could reach a prediction accuracy of 89.92%. Furthermore, the F1-score (0.92), precision (93.55%), and recall (97.32%) findings showed the effectiveness of the proposed approach. The COVID-19 pandemic considerably impacts the likelihood of depression in later life in the elderly community. This explainable DNN model can help identify patients to start treatment on them early.

Background: We aimed to develop a model predicting the participation of the elderly in a cognitive health program using the random forest algorithm and presented baseline information for enhancing cognitive health. Methods: This study analyzed the raw data of Seoul Welfare Panel Study (SWPS) (20), which was surveyed by Seoul Welfare Foundation for the residents of Seoul from Jun 1st to Aug 31st, 2015. Subjects were 2,111 (879 men and 1232 women) persons aged 60 yr and older living in the community who were not diagnosed with dementia. The outcome variable was the intention to participate in a cognitive health promotion program. A prediction model was developed by the use of a Random forests and the results of the developed model were compared with those of a decision tree analysis based on classification and regression tree (CART). Results: The random forests model predicted education level, subjective health, subjective friendship, subjective family bond, mean monthly family income, age, smoking, living with a spouse or not, depression history, drinking, and regular exercise as the major variables. The analysis results of test data showed that the accuracy of the random forests was 72.3% and that of the CART model was 70.9%. Conclusion: It is necessary to develop a customized health promotion program considering the characteristics of subjects in order to implement a program effectively based on the developed model to predict participation in a cognitive health promotion program.

Background: The objectives of this study were to systematically review the effects of grief-focused interventions, applied to dementia caregivers, on emotional burden including sadness and to provide baseline information for dementia caregivers to maintain mental health by identifying the effect size of intervention using a meta-analysis. Methods: We evaluated literature published from Jan 1, 2000, to Jun 28, 2019. Consequently, we selected five studies to conduct a systematic review. The Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields was used for conducting a quality assessment on the selected literature. Results: The results of the quality assessment showed that the score ranged from 11 to 24 points: one strong, one good, two adequate, and one limited. When the meta-mean was compared before and after grief intervention, the effect size of the random-effect model was 0.31 (95% CI: -0.07, 0.69) showed ‘intermediate effect’, but the confidence interval was not significant. Conclusion: The grief intervention for dementia caregivers revealed a moderate effect but it was not significant. In order to prove the effectiveness of grief intervention for dementia caregivers, a meta-analysis targeting RCT studies is needed.

To understand the changes in the lives of adults living in local communities due to the COVID-19 pandemic, it is necessary to identify subjective life satisfaction and to understand key factors affecting life satisfaction. This study identified the effect on life satisfaction of COVID-19 using epidemiological data representing adults in South Korean communities and developed a model for predicting the factors adversely affecting life satisfaction by applying a Bayesian nomogram. The subjects of this study were 227,808 adults who were 19 years old or older. Life satisfaction was measured in units of 10 points from 0 to 100: a score of 30 or less corresponding to −1 standard deviations was reclassified as dissatisfied, and a score of 40 or more was reclassified as satisfied. The nomogram developed in this study showed that “females who were between 30 and 39 years old, living in urban areas, with fewer meetings and sleeping hours, concerned about infection for themselves and the weak in the family due to the COVID-19 pandemic, concerned about death, with a mean household monthly income of KRW 3–5 million, who were non-smokers, with poor subjective health, and an education level of college graduation or above” would have a 66% chance of life dissatisfaction due to the COVID-19 pandemic. The results of this study suggest that the government needs not only to provide economic support but also to support education on infectious diseases and customized psychological counseling programs for those at high risk of life dissatisfaction after the COVID-19 pandemic.

Background: The evidence of transcranial direct current stimulation (tDCS) treatment effects on dementia is still insufficient. This study aimed to prove the scientific basis of tDCS by conducting a meta-analysis of previous studies that examined the effects of tDCS on the naming of patients with dementia. Methods: The literature search was conducted for publications published from Jan 2000 to Jun 2019 using four academic databases (PubMed, Web of Science, MEDLINE, and Cochrane Library). This study found 129 publications by searching academic databases according to the PRISMA protocol. Among them, irrelevant studies were excluded, and finally, 4 studies were meta-analyzed. Results: The score of the quality assessment on five studies ranged from 21 to 26 points, rated good or better. Meta-analysis results, tDCS had no significant effect on the improvement of naming ability of dementia. On the other hand, the effect size of the tDCS intervention + language/cognitive training was significant ‘large effect (SMD=0.72, 95% CI: 0.05, 1.39)’. Conclusion: This combination of tDCS and language/cognitive training significantly improved the naming ability of dementia patients.

People living in local communities have become more worried about infection due to the extended pandemic situation and the global resurgence of COVID-19. In this study, the author (1) selected features to be included in the nomogram using AdaBoost, which had an advantage in increasing the classification accuracy of single learners and (2) developed a nomogram for predicting high-risk groups of coronavirus anxiety while considering both prediction performance and interpretability based on this. Among 210,606 adults (95,287 males and 115,319 females) in South Korea, 39,768 people (18.9%) experienced anxiety due to COVID-19. The AdaBoost model confirmed that education level, awareness of neighbors/colleagues’ COVID-19 response, age, gender, and subjective stress were five key variables with high weight in predicting anxiety induced by COVID-19 for adults living in South Korean communities. The developed logistic regression nomogram predicted that the risk of anxiety due to COVID-19 would be 63% for a female older adult who felt a lot of subjective stress, did not attend a middle school, was 70.6 years old, and thought that neighbors and colleagues responded to COVID-19 appropriately (classification accuracy = 0.812, precision = 0.761, recall = 0.812, AUC = 0.688, and F-1 score = 0.740). Prospective or retrospective cohort studies are required to causally identify the characteristics of anxiety disorders targeting high-risk COVID-19 anxiety groups identified in this study.

It is essential to understand the voice characteristics in the normal aging process to accurately distinguish presbyphonia from neurological voice disorders. This study developed the best ensemble-based machine learning classifier that could distinguish hypokinetic dysarthria from presbyphonia using classification and regression tree (CART), random forest, gradient boosting algorithm (GBM), and XGBoost and compared the prediction performance of models. The subjects of this study were 76 elderly patients diagnosed with hypokinetic dysarthria and 174 patients with presbyopia. This study developed prediction models for distinguishing hypokinetic dysarthria from presbyphonia by using CART, GBM, XGBoost, and random forest and compared the accuracy, sensitivity, and specificity of the development models to identify the prediction performance of them. The results of this study showed that random forest had the best prediction performance when it was tested with the test dataset (accuracy = 0.83, sensitivity = 0.90, and specificity = 0.80, and area under the curve (AUC) = 0.85). The main predictors for detecting hypokinetic dysarthria were Cepstral peak prominence (CPP), jitter, shimmer, L/H ratio, L/H ratio_SD, CPP max (dB), CPP min (dB), and CPPF0 in the order of magnitude. Among them, CPP was the most important predictor for identifying hypokinetic dysarthria.

The rapid eye movement sleep behavior disorder (RBD) of Parkinson’s disease (PD) patients can be improved with medications such as donepezil as long as it is diagnosed with a thorough medical examination, since identifying a high-risk group of RBD is a critical issue to treat PD. This study develops a model for predicting the high-risk groups of RBD using random forest (RF) and provides baseline information for selecting subjects for polysomnography. Subjects consisted of 350 PD patients (Parkinson’s disease with normal cognition (PD-NC) = 48; Parkinson’s disease with mild cognitive impairment (PD-MCI) = 199; Parkinson’s disease dementia (PDD) = 103) aged 60 years and older. This study compares the prediction performance of RF, discriminant analysis, classification and regression tree (CART), radial basis function (RBF) neural network, and logistic regression model to select a final model with the best model performance and presents the variable importance of the final model’s variable. As a result of analysis, the sensitivity of RF (79%) was superior to other models (discriminant analysis = 14%, CART = 32%, RBF neural network = 25%, and logistic regression = 51%). It was confirmed that age, the motor score of Untitled Parkinson’s Disease Rating (UPDRS), the total score of UPDRS, the age when a subject was diagnosed with PD first time, the Korean Mini Mental State Examination, and Korean Instrumental Activities of Daily Living, were major variables with high weight for predicting RBD. Among them, age was the most important factor. The model for predicting Parkinson’s disease RBD developed in this study will contribute to the screening of patients who should receive a video-polysomnography.

It is necessary to identify how to improve the swallowing-related quality of life, as well as the swallowing function, in order to evaluate the effect of treatments on swallowing disorders. This study aimed to prove the effects of a compound swallowing intervention (Mendelsohn maneuver + neuromuscular electrical stimulation (NMES)) on the swallowing function and the quality of life by applying the compound swallowing intervention to patients with sub-acute swallowing disorders due to cerebral infarction for eight weeks. This study analyzed 43 subjects who were diagnosed with swallowing disorders due to cerebral infarction. The experiment consisted of the Mendelsohn maneuver treatment group (n = 15), the NMES treatment group (n = 13), the compound intervention group (Mendelsohn maneuver + NMES; n = 15). The results of ANCOVA showed that the changes in Functional Dysphagia Scale (FDS) scores and Swallowing−Quality of Life (SWAL−QOL) score were different among groups. The compound intervention group had the highest FDS scores and SWAL−QOL score followed by Mendelsohn, and the NMES group had the lowest. The result of this study suggests that NMES can be more effective when it is combined with a traditional swallowing rehabilitation therapy rather than a single intervention method.

The relationship between second-hand smoking and laryngopathy has not yet been reported. Thus, this study investigates the relationship between second-hand smoking and laryngopathy and suggests basic empirical data to prevent laryngopathy.This study analyzed 1,905 non-smokers over the age of 19 (269 men and 1,636 women) who completed the health questionnaire, laryngeal endoscope test, and urine cotinine test in the 2008 Korea National Health and Nutrition Examination Survey (KNHANES). Second-hand smoking was defined as a urine cotinine concentration of 50ng/ml and over. Confounding factors included age, gender, education, household income, occupation, alcohol consumption, and coffee consumption. Risk ratios (RR) and 95% confidence intervals (CI) were presented for the relationship between second-hand smoking and laryngopathy by using Poisson regression analysis.There was a significant relationship between second-hand smoking and laryngopathy (p

This study investigated the relationship between smoking and depression and anxiety using data from a nationwide survey representing Korean adolescents. Subjects were 6,489 adolescents in middle and high school (age 13–18) who had participated in the 2011 Korean Study of Promotion Policies on Children and Adolescents—Mental Health (KSPCAM). Daily smoking number of times for current smokers was classified as 1–2 times, 2–4 times and over 5 times. The odds ratio for the statistical test was presented using hierarchical logistic regression. When adjusted for covariates (gender, age, household economy, type of residing city, type of school, school record, satisfaction with school life, subjective health status, satisfaction with relationship with parents, and drinking experience), smokers more significantly likely to have depression (OR = 1.27, 95% CI [1.02–1.57]), and anxiety (OR = 1.49, 95% CI [1.14–1.96]) than non-smokers (p < 0.05). In addition, adolescents who smoke more than 5 cigarettes a day were 1.5 times more likely to have depression (OR = 1.48, 95% CI [1.13–1.92]) and anxiety (OR = 1.49, 95% CI [1.07–2.08]) than those who do not smoke. Smoking in adolescence was found to be significantly related with depression and anxiety. To promote the mental health of adolescents, effective smoking cessation programs are required.

This study aims to investigate the relationship between current smoking and lifetime amount smoked and the incidence of dysphonia using data from a national cross-sectional survey that represents the Korean population. Subjects were 3,600 non-institutionalised civilian adults over the age of 19 (1,501 males and 2,099 females) who completed the laryngeal examination of the 2008 Korea National Health and Nutrition Examination Survey (KNHANES). For lifetime amount smoked, subjects were classified as light smokers (≤26.7 pack years), medium smokers (26.7–40.5 pack years), heavy smokers (40.5–55.5 pack years), and very heavy smokers (55.5–156 pack years) based on pack years (packs smoked per day × years as a smoker). The odds ratio (OR) for the statistical test was presented using hierarchical logistic regression. When adjusted for covariates (age, gender, level of education, income, occupation, alcohol consumption and pain/discomfort during the last two weeks), current smokers had a 1.8 times (OR = 1.77, 95% CI [1.17–2.68]) higher risk for self-reported voice problems than non-smokers. Moreover, current smokers had a 1.6 times (OR = 1.56, 95% CI [1.02–2.38]) higher risk of laryngeal disorder (p < 0.05). In terms of pack years, very heavy smokers were significantly more likely to have laryngeal disorder, while self-reported voice problems were significantly more likely for heavy smokers but not very heavy smokers. The results of this study imply that chronic smoking has a significant relationship with dysphonia. Longitudinal studies are required in future work to verify the causality between lifetime smoking amount and dysphonia.

PurposeThis study investigated the relationship between 2-year-old children's exposure to TV and language delay.MethodsThe subjects of this study were 1,778 toddlers (906 males and 872 females) who participated in the Panel Study on Korean Children conducted in 2010. The linguistic ability of the toddlers was measured with the K-ASQ (Korean-Ages and Stages Questionnaire). The relationship between the amount of young children's exposure to TV and language delay was analyzed with Poisson regression.ResultsThe average daily TV watching time of 2-year-old Korean toddlers in this study was 1.21 hours. After all confounding variables were adjusted, toddlers with over 2 hours and less than 3 hours of TV watching time had 2.7 times more risk (RR = 2.74, 95% CI: 1.13-6.65) of language delay than those with less than 1 hour of TV watching time. Those with more than 3 hours of TV watching time had approximately 3 times (RR = 3.03, 95% CI: 1.12-8.21) more risk (pConclusionTwo-year-old Korean toddlers' average daily TV watching time of more than 2 hours was related with language delay.

Although distortion of weight perceptions has been known as a risk factor for adolescent depression, little has been known about the relationship between weight misperception patterns and depressive symptoms. This study explored the relationship between distortion of weight misperception patterns and depressive symptoms in Korean adolescents.The subjects of this study were 109,373 middle school students who participated in the Korea Youth Risk Behavior Web-based Survey (KYRBWS) from 2011 to 2013. By comparing the difference between Body Mass Index (BMI) and subjective perceptions of body weight, misperceptions of weight were classified into two categories: misperception of underweight and misperception of overweight.When confounding variables were adjusted, the results of the logistic regression analysis revealed that male students who perceived themselves as underweight despite their normal weight were 110% more likely to have depressive symptoms (OR = 1.10, 95% CI: 1.03-1.18) than male students with accurate weight perceptions. On the contrary, for female students, misperceptions of underweight had no significant relationship with depression symptoms. Regarding misperceptions of overweight, female students who perceived themselves as overweight despite their normal weight were 107% (OR = 1.07, 95% CI: 1.02-1.11) more likely to have depressive symptoms than female students with accurate weight perceptions. Moreover, female students who perceived themselves as overweight when they were underweight were 137% (OR = 1.37, 95% CI: 1.18-1.58) more likely to have depressive symptoms.Male students who underestimate their body weight and female students who overestimate their body weight were at a greater risk of depression emotions than students with accurate weight perceptions.