Hybrid Deep Learning and Squirrel Search Algorithm for Early Prediction of Heart Disease with IoT-Integrated Health Monitoring Systems

Abstract

This research proposes a hybrid approach combining deep learning models with the Squirrel Search Algorithm (SSA) for early heart disease prediction. Integrating Internet of Things (IoT) devices for real-time health monitoring enhances predictive capability and provides continuous patient data. The proposed model optimizes feature selection using SSA, ensuring that the most relevant and impactful features are used for prediction, which improves the accuracy and efficiency of the deep learning models. This study aims to provide a robust, real-time heart disease prediction system that can be seamlessly integrated into modern healthcare infrastructures.