Search

Heart disease has recently become a leading cause of death worldwide, making the prediction of cardiovascular diseases a challenging task in clinical data analysis. With the rapid increase in the size and complexity of medical datasets, automated models leveraging data mining and machine learning techniques are proving essential for clinicians to make accurate and timely decisions. Key challenges in heart disease prediction include imbalanced datasets and the insufficient significance of certain features. The goal of this research is to propose effective feature optimization and classification methods to enhance heart disease prediction, facilitating early diagnosis for medical professionals. Initially, the min-max normalization technique is applied to rescale data from the Shahid Rajaei hospital and UCI Cleveland datasets. Then, optimal features and feature subsets are identified using the Mutual Information technique and the Improved Particle Swarm Optimization (IPSO) algorithm. Finally, the optimized feature subsets are fed into an ensemble classifier to predict the presence or absence of heart disease. Experimental results show that the proposed IPSO-based ensemble model achieved accuracy rates of 98.41% and 97.40% on the UCI Cleveland and Shahid Rajaei hospital datasets, respectively, outperforming traditional machine learning techniques. [ABSTRACT FROM AUTHOR]

Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research and drug discovery. Some experimental techniques, such as methyl-specific antibodies, chromatin immune precipitation and mass spectrometry, exist for predicting protein methylation sites, but these techniques are time-consuming and costly. The ability to predict methylation sites using in silico techniques may help researchers identify potential candidate sites for future examination and make it easier to carry out site-specific investigations and downstream characterizations. In this research, we proposed a novel deep learning-based predictor, named DeepPRMS, to identify protein methylation sites in primary sequences. The DeepPRMS utilizes the gated recurrent unit (GRU) and convolutional neural network (CNN) algorithms to extract the sequential and spatial information from the primary sequences. GRU is used to extract sequential information, while CNN is used for spatial information. We combined the latent representation of GRU and CNN models to have a better interaction among them. Based on the independent test data set, DeepPRMS obtained an accuracy of 85.32%, a specificity of 84.94%, Matthew's correlation coefficient of 0.71 and a sensitivity of 85.80%. The results indicate that DeepPRMS can predict protein methylation sites with high accuracy and outperform the state-of-the-art models. The DeepPRMS is expected to effectively guide future research experiments for identifying potential methylated protein sites. The web server is available at http://deepprms.nitsri.ac.in/. [ABSTRACT FROM AUTHOR]

BACKGROUND: Patient sentiment analysis aids in identifying issue areas, timely remediation, and improved patient care by the healthcare professional. The relationship between pain management and patient sentiment analysis is crucial to providing patients with high-quality medical care. Therefore, a self-reported pain level assessment is required for the smart healthcare framework to determine the best course of treatment. OBJECTIVE: An efficient method for a pain sentiment recognition system has been proposed based on the analysis of human facial emotion patterns of patients in the smart healthcare framework. METHODS: The proposed system has been implemented in four phases: (i) in the first phase, the facial regions of the observation patient have been detected using the computer vision-based face detection technique; (ii) in the second phase, the extracted facial regions are analyzed using deep learning based feature representation techniques to extract discriminant and crucial facial features to analyze the level of pain emotion of patient; (iii) the level of pain emotions belongs from macro to micro facial expressions, so, some advanced feature tunning and representation techniques are built along with deep learning based features such as to distinguish low to high pain emotions among the patients in the third phase of the implementation, (iv) finally, the performance of the proposed system is enhanced using the score fusion techniques applied on the obtained deep pain recognition models for the smart healthcare framework. RESULTS: The performance of the proposed system has been tested using two standard facial pain benchmark databases, the UNBC-McMaster shoulder pain expression archive dataset and the BioVid Heat Pain Dataset, and the results are compared with some existing state-of-the-art methods employed in this research area. CONCLUSIONS: From extensive experiments and comparative studies, it has been concluded that the proposed pain sentiment recognition system performs remarkably well compared to the other pain recognition systems for the smart healthcare framework. [ABSTRACT FROM AUTHOR]

The Lorenz model is one of the most studied dynamical systems. Chaotic dynamics of several modified models of the classical Lorenz system are studied. In this article, a new chaotic model is introduced and studied computationally. By finding the fixed points, the eigenvalues of the Jacobian, and the Lyapunov exponents. Transition from convergence behavior to the periodic behavior (limit cycle) are observed by varying the degree of the system. Also transiting from periodic behavior to the chaotic behavior are seen by changing the degree of the system.