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

• URL: http://arxiv.org/abs/2408.03151v1
• Date: Wed, 31 Jul 2024 14:12:27 GMT
• Title: Optimizing Disease Prediction with Artificial Intelligence Driven Feature Selection and Attention Networks
• Authors: D. Dhinakaran, S. Edwin Raja, M. Thiyagarajan, J. Jeno Jasmine, P. Raghavan,
• Abstract summary: This article introduces a pioneering ensemble feature selection model. At the heart of the proposed model lies the SEV-EB algorithm, a novel approach to optimal feature selection. An HSC-AttentionNet is introduced, allowing the model to capture both short-term patterns and long-term dependencies in health data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid integration of machine learning methodologies in healthcare has ignited innovative strategies for disease prediction, particularly with the vast repositories of Electronic Health Records (EHR) data. This article delves into the realm of multi-disease prediction, presenting a comprehensive study that introduces a pioneering ensemble feature selection model. This model, designed to optimize learning systems, combines statistical, deep, and optimally selected features through the innovative Stabilized Energy Valley Optimization with Enhanced Bounds (SEV-EB) algorithm. The objective is to achieve unparalleled accuracy and stability in predicting various disorders. This work proposes an advanced ensemble model that synergistically integrates statistical, deep, and optimally selected features. This combination aims to enhance the predictive power of the model by capturing diverse aspects of the health data. At the heart of the proposed model lies the SEV-EB algorithm, a novel approach to optimal feature selection. The algorithm introduces enhanced bounds and stabilization techniques, contributing to the robustness and accuracy of the overall prediction model. To further elevate the predictive capabilities, an HSC-AttentionNet is introduced. This network architecture combines deep temporal convolution capabilities with LSTM, allowing the model to capture both short-term patterns and long-term dependencies in health data. Rigorous evaluations showcase the remarkable performance of the proposed model. Achieving a 95% accuracy and 94% F1-score in predicting various disorders, the model surpasses traditional methods, signifying a significant advancement in disease prediction accuracy. The implications of this research extend beyond the confines of academia.

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