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Explainable Artificial Intelligence (XAI) enhances transparency and interpretability in AI models, which is crucial for trust and accountability in healthcare. A potential application of XAI is disease prediction using various data modalities. This study conducts a Systematic Literature Review (SLR) following the PRISMA protocol, synthesizing findings from 30 selected studies to examine XAI's evolving role in disease prediction. It explores commonly used XAI methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), and their impact across medical fields in disease prediction. The review highlights key gaps, including limited dataset diversity, model complexity, and reliance on single data types, emphasizing the need for greater interpretability and data integration. Addressing these issues is crucial for advancing AI in healthcare. This study contributes by outlining current challenges and potential solutions, suggesting directions for future research to develop more reliable and robust XAI methods. [ABSTRACT FROM AUTHOR]

The immune system plays a crucial role in the pathogenesis of neurodegenerative diseases. Here, we explored whether blood immune cell profiles are already altered in healthy individuals with a genetic predisposition to amyotrophic lateral sclerosis (ALS) or Alzheimer's disease (AD). Using multicolor flow cytometry, we analyzed 92 immune cell phenotypes in the blood of 448 healthy participants from the Berlin Aging Study II. We calculated polygenic risk scores (PGSs) using genome-wide significant SNPs from recent large genome-wide association studies on ALS and AD. Linear regression analyses were then performed of the immune cell types on the PGSs in both the overall sample and a subgroup of older participants (>60 years). While we did not find any significant associations between immune cell subtypes and ALS and AD PGSs when controlling for the false discovery rate (FDR = 0.05), we observed several nominally significant results (p < 0.05) with consistent effect directions across strata. The strongest association was observed with CD57+ CD8+ early-memory T cells and ALS risk (p = 0.006). Other immune cell subtypes associated with ALS risk included PD-1+ CD8+ and CD57+ CD4+ early-memory T cells, non-classical monocytes, and myeloid dendritic cells. For AD, naïve CD57+ CD8+ T cells and mature NKG2A+ natural killer cells showed nominally significant associations. We did not observe major immune cell changes in individuals at high genetic risk of ALS or AD, suggesting they may arise later in disease progression. Additional studies are required to validate our nominally significant findings. [ABSTRACT FROM AUTHOR]

Since December 2019, cases of COVID-19 have spread globally, caused millions of deaths and huge economic losses. To investigate the impact of different factors and predict the future trend, this study collects relevant data for 15 countries, containing 44 features in about 900 days, which can be classified into four groups: pandemic information, the characteristics of countries, climate, and prevention policies. Through the selection of several important features, we identified the factors that have stronger impact on the increase of new cases in different groups. Then, we use a long-time span data to predict the future COVID-19 new cases by training a long short-term memory (LSTM) model, a support vector regressor (SVR) and a temporal convolutional network (TCN), among which LSTM possessed the best performance and offered a good generalization ability. Under the metric of explained variance scores (EVS), the prediction performances were the most accurate for Germany (0.864), Italy (0.860) and the United States (0.766). Overall, the results of this study may provide insight for predictions of number of COVID-19 new cases in more countries/regions and offer some insightful recommendation for governments to carry out more effective policies to prevent COVID-19. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) and machine learning (ML) in medicine and healthcare has been extensively explored across various areas. AI and ML can revolutionize cardiovascular disease management by significantly enhancing diagnostic accuracy, disease prediction, workflow optimization, and resource utilization. This review summarizes current advancements in AI and ML concerning cardiovascular disease, including their clinical investigation and use in primary cardiac imaging techniques, common cardiovascular disease categories, clinical research, patient care, and outcome prediction. We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. Furthermore, this review emphasizes the transformative potential of AI and ML in cardiovascular practice by improving clinical decision making, reducing human error, enhancing patient monitoring and support, and creating more efficient healthcare workflows for complex cardiovascular conditions. [ABSTRACT FROM AUTHOR]

Ever since the rise of human civilization, more and more diseases have been discovered with the rapid growth of medical knowledge. This sheer volume of information makes it hard for humans to memorize or even utilize it efficiently. Thus, machine learning emerged as a powerful tool for complex calculations by offering a solution to this challenge. This paper intends to use deep learning and machine learning algorithms to develop a predictive model that can recognize potential diseases based on symptoms. The model is then seamlessly integrated into a text-based disease prediction assistant chatbot that serves as a communication platform between the users and the system. The algorithms researched for the disease prediction models are knearest neighbours (KNN), support vector machines (SVM), random forest, and neural networks. After that, a chatbot application is created by integrating long short-term memory (LSTM), natural language toolkit (NLTK) libraries, and Telegram. As a result, the SVM models demonstrated excellent performance by achieving an accuracy of 92.24%, closely followed by random forest with 92.23%, KNN with 91.57%, and artificial neural network (ANN) with 91.52% accuracy. In short, this paper presents a potential solution for a more accurate disease prediction tool by implementing the best disease prediction model with the chatbot models together. [ABSTRACT FROM AUTHOR]

Heart failure is a leading cause of premature death, especially among individuals with a sedentary lifestyle. Early and accurate detection is essential to prevent the progression of this situation. However, many existing prediction systems failed to detect early and accurately, also taking more time to detect. To address these issues, we propose an advanced heart failure detection model that combines one-dimensional chaotic maps and a Gradient-Based Optimizer (GBO) called Chaotic Gradient-Based Optimizer (CGBO). This approach improves feature selection by effectively selecting the most crucial features related to the risk of heart failure. Additionally, we introduce the Fuzzy Temporal Optimized Convolutional Neural Network (FTOCNN) classifier that incorporates CGBO and fuzzy temporal rules to enhance detection accuracy. The proposed model is evaluated using the UCI heart dataset and Electronic Health Records (EHRs) and its performance is assessed through statistical measures, classification metrics, and a Wilcoxon rank-sum p-test. Furthermore, a tenfold cross-validation process ensures a comprehensive evaluation and the proposed method outperforms different Machine Learning (ML) / Deep Learning (DL) classifiers. The experimental findings reveal that CGBO significantly improves the predictive performance of the FTOCNN classifier by achieving 94% accuracy in EHR and enhances the reliability of heart failure detection compared to existing systems. [ABSTRACT FROM AUTHOR]

Objective To construct an ARIMA (autoregressive moving average) model to predict the incidence of human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) in this region and evaluate the predictive efficacy of the model. Methods The ARIMA model was established for the monthly incidence data of HIV/AIDS in Yunnan province from January 2010 to December 2020. Through comparative analysis, the optimal fitting model of the incidence of AIDS and HIV was selected, and the monthly incidence of the whole year of 2020 was predicted, and the accuracy of the prediction model was analyzed. Results From 2010 to 2020,the incidence of AIDS in Yunnan province remained stable, while the incidence of HIV showed a downward trend year by year. After stabilizing treatment, unit root test (ADF) and model screening, ARIMA (2,0,2) (1,0,2) [12] was determined to be the best fitting model for AIDS incidence, and ARIMA (2,1,1) (2,1,0) [12] was determined to be the best fitting model for HIV incidence. Goodness-of-fit tests revealed that the R~2 were 0. 668 and 0. 737, Ljung-Box statistics were 12. 97 (P＞0. 05) and 14. 89 (P＞0. 05), Bayesian Information Criterion (BIC) were-3. 07 and-3. 08, and mean absoult percentage error (MAPE) were 16. 41 and 11. 29,respectively. The autocorrelation function graph (ACF) and the partial autocorrelation function graph (PACF) of the model residuals are both within the 95%CI range. The predicted value of the model curve was consistent with the actual value, and the predicted value was close to the actual value. Conclusion The ARIMA model has a good effect on the prediction of the incidence of AIDS and HIV. It can be used as an effective tool for short-term prediction and analysis, and provide scientific decision-making support for relevant departments to take effective AIDS prevention and control measures in time. [ABSTRACT FROM AUTHOR]

The integration of federated learning, attention-based models like BERT, and blockchain technology presents a transformative approach for managing medical records. This paper introduces a hybrid framework combining the latter technologies to solve critical challenges pertaining to the secure management of healthcare data. Federated learning provides a distributed learning of machine models, where sensitive patient data does not need to be transferred, while BERT models improve the precision in processing medical records using natural language understanding. Blockchain adds a layer of security by recording model updates transparently to ensure tamper-proofing and transactions. A concrete methodology for the implementation of the introduced framework including the design of the smart contract in Solidity is provided to secure recording the model updates. Various tests assessing the performance of the proposed system show a significant improvement in data privacy, model security and precision, compared to the other systems. This hybrid methodology offers advances in handling medical records and elaborates a new benchmark in integrating AI and blockchain for healthcare. This framework thus redefines secure and collaborative healthcare data management, setting the stage for further enhancements in privacy-focused AI applications in medical contexts. [ABSTRACT FROM AUTHOR]

There are critical transition phenomena during the progression of many diseases. Such critical transitions are usually accompanied by catastrophic disease deterioration, and their prediction is of significant importance for disease prevention and treatment. However, predicting disease deterioration solely based on a single sample is a difficult problem. In this study, we presented the network information gain (NIG) method, for predicting the critical transitions or disease state based on network flow entropy from omics data of each individual. NIG can not only efficiently predict disease deteriorations but also detect their dynamic network biomarkers on an individual basis and further identify potential therapeutic targets. The numerical simulation demonstrates the effectiveness of NIG. Moreover, our method was validated by successfully predicting disease deteriorations and identifying their potential therapeutic targets from four real omics datasets, i.e., an influenza dataset and three cancer datasets. [ABSTRACT FROM AUTHOR]

Abstract Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these “omics” studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data‐driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state‐of‐the‐art applications of ML algorithms for chronic disease detection and prediction across datasets, including medical imaging, genomics, wearables, and electronic health records. Specifically, we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures. We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field. While highlighting the critical innovations and successes emerging in this space, we identify the key challenges and limitations that remain to be addressed. Finally, we discuss pathways forward toward scalable, equitable, and clinically implementable ML solutions for transforming chronic disease screening and prevention.

This research offered an in-depth evaluation of "BambooShoots.AI," a platform aiding bamboo cultivators in harvest timing and pest detection. Using a quantitative method, it incorporated Lund A.M.'s USE Questionnaire (2001) and usage data analysis to gauge user satisfaction and effectiveness. The majority of participants were mid-aged cultivators, providing insights on system usefulness, ease of use, learning, and overall satisfaction. Demographics showed a primary user base of 35-44-year-olds with balanced gender representation and a high rate of Bachelor’s degree holders, highlighting the platform's broad appeal. BambooShoots.AI was noted for its significant usability, scoring well in all evaluated aspects. The study suggested enhancing the interface for older users, continuous feedback integration for improvement, and specialized training programs. It emphasized the need for accessible and inclusive design, aligning with evolving user needs. BambooShoots.AI emerged as a potent, user-focused tool in agricultural technology, pointing to its potential for wider adoption and development in the farming community, and affirming its role as a critical asset in modern agriculture.

ABSTRACT: The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high-frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 wk. A Random Forest classifier, using input features selected by the Boruta algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 wk and when using information from the last 3 wk, combined with a balanced accuracy of 79%. Removing sensor data results has a tendency to reduce the precision for predictions, especially when using information from the last 1, 2, or 3 wk. Moving to a larger dataset (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high-resolution data from other systems, such as automated milking systems.

Boosting algorithms are a set of machine learning techniques that are predicated on the notion that a weak learner's acquisition of multiple basic classifiers might yield results that are superior to those of any one simple classifier used alone. A comprehensive evaluation of regularly used boosting techniques against highly investigated diseases is lacking, despite the fact that boosting approaches have been used for disease prediction in many studies. Thus, the purpose of this work is to highlight the main algorithms and strategies in the boosting learning. The results of this work will help academics identify a more appropriate boosting approach to predict disease, as well as better understand current patterns and hotspots in diseases prediction models that use boosting learning. The results showed that adaboost algorithm outperformed other algorithms in terms of accuracy, achieving above 90%. This review also demonstrates how combining two boosting methods can increase the basic classifier's accuracy. By using AdaBoost and LightGBM, the accuracy reached 99.75%. XGBoost and Gradient Boosting techniques were employed more frequently in researches than other boosting algorithms.

The immune system likely plays a key role in Parkinson's disease (PD) pathophysiology. Thus, we investigated whether immune cell compositions are already altered in healthy individuals at high genetic risk for PD. We quantified 92 immune cell subtypes in the blood of 442 individuals using multicolor flow cytometry. Polygenic risk scores (PGS) for PD were calculated based on genome-wide significant SNPs (n = 87) from a large genome-wide association study (n = 1,530,403). Linear regression analyses did not reveal significant associations between PGS and any immune cell subtype (FDR = 0.05). Nominally significant associations were observed for NKG2C+ B cells (p = 0.026) in the overall sample. Older participants at increased genetic PD risk also showed a higher proportion of myeloid dendritic cells (p = 0.019) and CD27+CD4+ memory T cells (p = 0.043). Several immune cells were nominally statistically associated in women only. These findings suggest that major alterations of immune cells only occur later in the progression of PD. [ABSTRACT FROM AUTHOR]

This study proposes a new hybrid machine learning (ML) model for the early and accurate diagnosis of heart disease. The proposed model is a combination of two powerful ensemble ML models, namely ExtraTreeClassifier (ETC) and XGBoost (XGB), resulting in a hybrid model named ETCXGB. At first, all the features of the utilized heart disease dataset were given as input to the ETC model, which processed it by extracting the predicted probabilities and produced an output. The output of the ETC model was then added to the original feature space by producing an enriched feature matrix, which is then used as input for the XGB model. The new feature matrix is used for training the XGB model, which produces the final result that whether a person has cardiac disease or not, resulting in a high diagnosis accuracy for cardiac disease. In addition to the proposed model, three other hybrid DL models, such as convolutional neural network + recurrent neural network (CNN-RNN), convolutional neural network + long short-term memory (CNN-LSTM), and convolutional neural network + bidirectional long short-term memory (CNN-BLSTM), were also investigated. The proposed ETCXGB model improved the prediction accuracy by 3.91%, while CNN-RNN, CNN-LSTM, and CNN-BLSTM enhanced the prediction accuracy by 1.95%, 2.44%, and 2.45%, respectively, for the diagnosis of cardiac disease. The simulation outcomes illustrate that the proposed ETCXGB hybrid ML outperformed the classical ML and DL models in terms of all performance measures. Therefore, using the proposed hybrid ML model for the diagnosis of cardiac disease will help the medical practitioner make an accurate diagnosis of the disease and will help the healthcare society decrease the mortality rate caused by cardiac disease. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic severely impacted healthcare systems, affecting patient outcomes and resource allocation. This study applied automated machine learning (AutoML) to analyze key health outputs, such as discharge conditions, mortality, and COVID-19 cases, with the goal of improving responses to future crises. Methods: AutoML was used to train and validate models on an ICD-10 dataset covering the first wave of COVID-19 in Romania (January–September 2020). Results: For discharge outcomes, Light Gradient Boosted models achieved an F1 score of 0.9644, while for mortality 0.7545 was reached. A Generalized Linear Model blender achieved an F1 score of 0.9884 for "acute or emergency" cases, and an average blender reached 0.923 for COVID-19 cases. Older age, specific hospitals, and oncology wards were less associated with improved recovery rates, while mortality was linked to abnormal lab results and cardiovascular/respiratory diseases. Patients admitted without referral, or patients in hospitals in the central region and the capital region of Romania were more likely to be acute cases. Finally, counties such as Argeş (South-Muntenia) and Brașov (Center) showed higher COVID-19 infection rates regardless of age. Conclusions: AutoML provided valuable insights into patient outcomes, highlighting variations in care and the need for targeted health strategies for both COVID-19 and other health challenges. [ABSTRACT FROM AUTHOR]

Disease prediction using computer-based methods is now an established area of research. The importance of technological intervention is necessary for the better management of disease, as well as to optimize use of limited resources. Various AI-based methods for disease prediction have been documented in the literature. Validated AI-based systems support diagnoses and decision making by doctors/medical practitioners. The resource-efficient dissemination of the symptoms identified and the diagnoses undertaken is the requirement of the present-day scenario to support paperless, yet seamless, information sharing. The representation of symptoms using grammar provides a novel way for the resource-efficient encoding of disease diagnoses. Initially, symptoms are represented as strings, and, in terms of grammar, this is called a sentence. Moreover, the conversion of the generated string containing the symptoms and the diagnostic outcome to a QR code post encryption makes it portable. The code can be stored in a mobile application, in a secure manner, and can be scanned wherever required, universally. The patient can carry the medical condition and the diagnosis in the form of the QR code for medical consultations. This research work presents a case study based on two diseases, influenza and coronavirus, to highlight the proposed methodology. Both diseases have some common and overlapping symptoms. The proposed system can be implemented for any kind of disease detection, including clinical and diagnostic imaging. [ABSTRACT FROM AUTHOR]

Purpose: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analysis, but to date, most AI algorithms have only been applied to analyzing image data in ophthalmologic practice. In this review we aimed to characterize the use of AI in the analysis of EHR, and to critically appraise the adherence of each included study to the CONSORT-AI reporting guideline. Methods: A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline. Results: Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8–14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%). Conclusions: In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making. [ABSTRACT FROM AUTHOR]

Patient representation learning based on electronic health records (EHR) is a critical task for disease prediction. This task aims to effectively extract useful information on dynamic features. Although various existing works have achieved remarkable progress, the model performance can be further improved by fully extracting the trends, variations, and the correlation between the trends and variations in dynamic features. In addition, sparse visit records limit the performance of deep learning models. To address these issues, we propose the multi-perspective patient representation Extractor (MPRE) for disease prediction. Specifically, we propose frequency transformation module (FTM) to extract the trend and variation information of dynamic features in the time–frequency domain, which can enhance the feature representation. In the 2D multi-extraction network (2D MEN), we form the 2D temporal tensor based on trend and variation. Then, the correlations between trend and variation are captured by the proposed dilated operation. Moreover, we propose the first-order difference attention mechanism (FODAM) to calculate the contributions of differences in adjacent variations to the disease diagnosis adaptively. To evaluate the performance of MPRE and baseline methods, we conduct extensive experiments on two real-world public datasets. The experiment results show that MPRE outperforms state-of-the-art baseline methods in terms of AUROC and AUPRC. [ABSTRACT FROM AUTHOR]

Effectively and equitably allocating medical resources, particularly for minority groups, is a critical issue that warrants further investigation in rural hospitals. Machine learning techniques have gained significant traction and demonstrated strong performance across various fields in recent years. The determination of hyperparameters significantly influences the performance of machine learning models. Thus, this study employs Optuna, a framework specifically designed for optimizing the hyperparameters of machine learning models. Building on prior research, machine learning models with Optuna (MLOPTA) are introduced to forecast diseases of indigenous patients. The numerical results reveal that the designed MLOPTA system can accurately capture the occurrences of specified diseases. Therefore, the MLOPTA system offers a promising approach for disease forecasting. The disease forecasting results can serve as crucial references for allocating hospital resources. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) have become a major public health problem affecting the national economy and social development, and have become one of the major causes of death. Therefore, the prevention, control and risk assessment of CVD have been increasingly emphasized. However, current CVD prediction models face limitations in capturing complex relationships within physiological data, potentially hindering accurate risk assessment. This study addresses this gap by proposing a novel Framework for Multi-Input, One-dimensional Convolutional Neural Network (1D-CNN) with Attention Mechanism for CVD (FMI-CAECD). This framework leverages the feature extraction capabilities of Convolutional Neural Network (CNN) alongside an Attention Mechanism to adaptively identify critical features and non-linear relationships within the data. Additionally, Shapley Additive Explanations (SHAP) analysis is incorporated to provide deeper insights into individual feature importance for disease prediction. Performance evaluation on the BRFSS 2022 dataset demonstrates that FMI-CAECD achieves superior accuracy (97.45%), sensitivity (96.84%), specificity (95.07%), and F1-score (92.44%) compared to traditional machine learning baselines and other deep learning models. These findings suggest that FMI-CAECD offers a promising approach for CVD risk assessment. [ABSTRACT FROM AUTHOR]

The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the accuracy is impacted by a lot of factors when predicting host phenotypes with the metagenomic data, e.g. small sample size, class imbalance, high-dimensional features, etc. To address these challenges, we propose MicroHDF, an interpretable deep learning framework to predict host phenotypes, where a cascade layers of deep forest units is designed for handling sample class imbalance and high dimensional features. The experimental results show that the performance of MicroHDF is competitive with that of existing state-of-the-art methods on 13 publicly available datasets of six different diseases. In particular, it performs best with the area under the receiver operating characteristic curve of 0.9182 ± 0.0098 and 0.9469 ± 0.0076 for inflammatory bowel disease (IBD) and liver cirrhosis, respectively. Our MicroHDF also shows better performance and robustness in cross-study validation. Furthermore, MicroHDF is applied to two high-risk diseases, IBD and autism spectrum disorder, as case studies to identify potential biomarkers. In conclusion, our method provides an effective and reliable prediction of the host phenotype and discovers informative features with biological insights. [ABSTRACT FROM AUTHOR]

The widespread issue of data imbalance in healthcare classification tasks presents a significant challenge, as minority class instances, often representing critical yet less firequent medical conditions such as rare diseases, are overshadowed by the majority class. This imbalance can lead to poor predictive performance in medical diagnostics, where accurately identifying rare but severe conditions is crucial for patient outcomes. To address this, this study introduces a stacking ensemble classifier based on a Generative Adversarial Network (GAN), enhanced by the Shufed Frog Leaping Algorithm (SFLA). The core innovation of this approach lies in the utilization of SFLA to optimize the GAN's generative process, producing highly representative synthetic instances of the minority class. These synthetic instances enhance the authenticity of rare disease cases, which are often underrepresented in clinical datasets. By refining these instances through iterative interactions with the stacking ensemble, the GAN adapts them to closely resemble misclassified samples, thereby improving the system's diagnostic accuracy. This adaptive integration of SFLA and GANs results in a more robust ensemble classifier specifically tailored to handle the complexities of medical data. The technique was tested on imbalanced medical datasets, including those for thyroid and skin disorders, yielding an F1-score and AUC between 0.8 and 1. It outperformed the compared algorithms by 10-15%, significantly enhancing the accuracy of healthcare system evaluations, particularly for rare conditions. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) requires early and precise diagnosis to manage and prevent long-term health consequences effectively. In this research, a large dataset of healthcare data gathered from various hospitals in Kerala, India, was evaluated using multiple machine learning (ML) and deep learning (DL) models to identify a highly reliable and accurate prediction of PCOS. The six algorithms used for comparison with the proposed DL model are support vector classification, random forest, logistic regression, k-nearest neighbors, and gaussian naive Bayes; they were selected due to their strengths in handling features in large datasets. The highly parameterized neural networks were tuned using efficient approaches like Optuna and genetic algorithms. The results indicated that the model implemented using our proposed combination of DL model and Optuna, outperformed the traditional models, achieving 93.55% reliability. This suggests the potential for using deep learning for decision-making in diagnosing PCOS. This method demonstrates the importance of integrating various data types with powerful analytic tools in medical diagnostics to support customized therapy. [ABSTRACT FROM AUTHOR]

This research offered an in-depth evaluation of "BambooShoots.AI," a platform aiding bamboo cultivators in harvest timing and pest detection. Using a quantitative method, it incorporated Lund A.M.'s USE Questionnaire (2001) and usage data analysis to gauge user satisfaction and effectiveness. The majority of participants were mid-aged cultivators, providing insights on system usefulness, ease of use, learning, and overall satisfaction. Demographics showed a primary user base of 35-44-year-olds with balanced gender representation and a high rate of Bachelor's degree holders, highlighting the platform's broad appeal. BambooShoots.AI was noted for its significant usability, scoring well in all evaluated aspects. The study suggested enhancing the interface for older users, continuous feedback integration for improvement, and specialized training programs. It emphasized the need for accessible and inclusive design, aligning with evolving user needs. BambooShoots.AI emerged as a potent, user-focused tool in agricultural technology, pointing to its potential for wider adoption and development in the farming community, and affirming its role as a critical asset in modern agriculture. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things with the Cloud improves our lives by facilitating smooth connections between people and items. Predictive analytics, fueled by cutting-edge machine learning and artificial intelligence, turns reactive healthcare initiatives into proactive ones. A subset of machine learning called deep learning is essential for quickly analyzing large datasets, producing insightful conclusions, and efficiently addressing challenging problems. For early interventions and preventive care, especially for those who are at risk, accurate and timely illness prediction is crucial. Making accurate prediction models becomes crucial when utilizing electronic medical records. Accuracy is improved by using deep learning variations of recurrent neural networks that can handle sequential time-series data. Predictive analytics is applied to cloud-stored electronic medical records and data from Internet of Things devices in this suggested system. With a remarkable accuracy of 98.86%, the smart healthcare system is intended to monitor and anticipate the risk of heart disease utilizing Bi-LSTM (bidirectional long short-term memory). Furthermore, it reaches 98.9% accuracy, 98.8% sensitivity, 98.89% specificity, and 98.86% F-measure. These outcomes greatly surpass the performance of current smart heart disease prediction systems. [ABSTRACT FROM AUTHOR]

As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) have gained significant attention in disease prediction where the latent embeddings of patients are modeled as nodes and the similarities among patients are represented through edges. The graph structure, which determines how information is aggregated and propagated, plays a crucial role in graph learning. Recent approaches typically create graphs based on patients' latent embeddings, which may not accurately reflect their real-world closeness. Our analysis reveals that raw data, such as demographic attributes and laboratory results, offers a wealth of information for assessing patient similarities and can serve as a compensatory measure for graphs constructed exclusively from latent embeddings. In this study, we first construct adaptive graphs from both latent representations and raw data respectively, and then merge these graphs via weighted summation. Given that the graphs may contain extraneous and noisy connections, we apply degree-sensitive edge pruning and kNN sparsification techniques to selectively sparsify and prune these edges. We conducted intensive experiments on two diagnostic prediction datasets, and the results demonstrate that our proposed method surpasses current state-of-the-art techniques. • We propose a novel graph construction using multimodal embeddings and patient features via weighted sum. • We use edge pruning and kNN sparsification to reduce redundant and noisy edges in graph creation. • We design a novel loss function to optimize modality-shared and specific representations separately. • Our method outperforms others on two datasets; ablation studies and visualizations confirm its effectiveness. [ABSTRACT FROM AUTHOR]

As the global population ages, healthcare systems face increasing challenges in managing the complex health needs of older adults, including multimorbidity, cognitive decline, and frailty. Artificial intelligence (AI) holds significant potential to address these challenges by offering advanced tools for personalized health management, disease prediction, and real-time monitoring. This paper reviews key AI applications in gerontology, focusing on its role in analyzing multimodal data such as electronic health records, genomic data, medical imaging, and wearable device metrics. AI's ability to integrate and analyze these diverse data types enhances the precision of disease management and treatment personalization, particularly in chronic disease care and cognitive function assessment. However, challenges related to data quality, privacy concerns, and model interpretability remain. This review highlights both the transformative potential and the limitations of AI in elderly healthcare, advocating for future research aimed at improving model transparency, scalability, and interdisciplinary integration to enhance geriatric care. [ABSTRACT FROM AUTHOR]

Theileriosis, a parasitic disease, caused by Theileria spp. and transmitted through ticks, poses a significant threat to livestock, leading to elevated morbidity and mortality rates. This study investigated the incidence trend of theileriosis in Kerala, India, over three years (2019–21). Notably, the research unveiled a substantial upsurge in bovine theileriosis cases within Kerala during this period, partly attributed to the state’s severe flooding and landslides in 2018, triggered by incessant monsoon rains. The present study envisaged pinpointing the risk factors underlying the prevalence of theileriosis in Kerala. Employing linear discriminant analysis, key environmental and remote sensing variables influencing the disease’s incidence were identified. Subsequently, these risk factors underwent climate disease modelling, leading to the creation of risk maps. To predict areas sensitive to theileriosis outbreaks in Kerala, two regression models and nine machine learning models were employed. The gradient boost and random forest models demonstrated the most accurate fit among these. The study also estimated the basic reproduction number (R0), which ranged from 0.89 to 1.8. This value indicates a high potential for Theileria spp. transmission within the study area. Consequently, the research outcomes offer valuable insights into pinpointing high risk theileriosis locations in livestock in Kerala. [ABSTRACT FROM AUTHOR]