Predicting heart disease based on an intelligent healthcare monitoring system using HPM-NIA

Before a heart attack happens, treating cardiac patients effectively depends on precise heart disease prediction. A heart disease prediction system for the determination of whether the patient has a heart disease condition or not is to be developed. The paper employs a diverse array of models within the heart disease prediction system, each serving a specific purpose to enhance accuracy and efficiency. Data pre-processing techniques, including normalization, standardization, and missing value removal, ensure the quality and consistency of the dataset. Feature extraction methods such as mean, median, standard deviations, and higher-order statistical features contribute to extracting informative features crucial for prediction. The Multi-Objective Forest Particle Swarm Optimization (MOFPSO) algorithm (hybrid version of Forest Optimization (FO) and Particle Swarm Optimization (PSO)) is introduced for efficient feature selection, balancing predictive accuracy and model complexity. Finally, the prediction model, Naïve Bayes trained with Meliorated Ant Colony Optimization algorithm (NB-MACO), is implemented for its simplicity and effectiveness in handling medical datasets. This integration fine-tunes the Naïve Bayes classifier’s hyperparameters, optimizing its performance and resulting in an accuracy of 91.974%. The collective utilization of these models and techniques ensures the development of a robust and accurate heart disease prediction system.