  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.
