Optimizing Disease Prediction with Artificial Intelligence Driven Feature Selection and Attention Networks

Deleted Journal, Journal Year: 2024, Volume and Issue: 20(3s), P. 12 - 27

Published: April 4, 2024

The rapid integration of machine learning methodologies in healthcare has ignited innovative strategies for disease prediction, particularly with the vast repositories Electronic Health Records (EHR) data. This article delves into realm multi-disease presenting a comprehensive study that introduces pioneering ensemble feature selection model. model, designed to optimize systems, combines statistical, deep, and optimally selected features through Stabilized Energy Valley Optimization Enhanced Bounds (SEV-EB) algorithm. objective is achieve unparalleled accuracy stability predicting various disorders. work proposes an advanced model synergistically integrates features. combination aims enhance predictive power by capturing diverse aspects health At heart proposed lies SEV-EB algorithm, novel approach optimal selection. algorithm enhanced bounds stabilization techniques, contributing robustness overall prediction To further elevate capabilities, HSC-AttentionNet introduced. network architecture deep temporal convolution capabilities LSTM, allowing capture both short-term patterns long-term dependencies Rigorous evaluations showcase remarkable performance Achieving 95% 94% F1-score disorders, surpasses traditional methods, signifying significant advancement accuracy. implications this research extend beyond confines academia. By harnessing wealth information embedded EHR data, presents paradigm shift interventions. optimized diagnosis treatment pathways facilitated hold promise more accurate personalized healthcare, potentially revolutionizing patient outcomes

Language: Английский

A Unified Framework for Neurological Disease Detection and Gait Classification Using Deep Graph Learning

International journal of intelligent engineering and systems, Journal Year: 2024, Volume and Issue: 17(3), P. 527 - 538

Published: May 3, 2024

This paper presents Dynamic Gait Signature Analysis (DGSA), an innovative approach to gait analysis that leverages deep graph learning techniques.Unlike conventional methods, DGSA multifaceted parameters and advanced techniques, such as Graph Convolutional Networks (GCNs) Attention (GATs).These techniques enable a comprehensive of dynamics, including the use dynamic representation methods like Cycle Joint Angles Power Graph.DGSA's unique framework allows for simultaneous prediction neurological diseases, classification, early detection cognitive impairments.By modeling structures, captures intricate relationships between body movements foot positions, ultimately enhancing accuracy in classification tasks.Comprehensive experiments on real-world datasets demonstrate DGSA's robustness, generalization, superiority accuracy.Our achieves notable metrics: velocity (1.6 m/s), stability margin (5.6 cm), variability (2.4%), joint range motion (56 degrees), balance index (0.4), minimum toe clearance (2.3 progression angle (8.6 stiffness (172).This study includes comparative approaches based these key performance metrics, demonstrating significant advancement methodology.

Language: Английский

The Future of Well‐Being

Published: Dec. 30, 2024

The chapter explores the dynamic realm of AI technologies in wellness management, addressing critical facets such as data privacy, security, fairness machine learning models, and overall system performance. Commencing with a comprehensive overview AI's role personalized wellness, emphasizing leverage personal health data, then navigates intricate landscape privacy. Examining evolving regulations ethical considerations, work delves into consequences breaches healthcare, advocating for robust security measures, including encryption access controls. Ethical within domain are thoroughly explored, biases, identification techniques, crucial diverse datasets fostering equitable outcomes. Navigating legal landscape, scrutinizes frameworks related to non-discrimination, ensuring compliance privacy laws GDPR. Crucially, integrates detailed performance evaluation, assessing model accuracy, preservation, fairness, efficiency. Metrics differential parameters, indistinguishability contributions, scalability rigorously evaluated, system's optimal resource utilization real-time adaptability. abstract concludes by summarizing key points on AI-driven management. A resounding call action urges collaboration among practitioners, researchers, policymakers forge responsible, framework, where well-being individuals is championed through conscientious integration technologies, both efficacy

Language: Английский