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Optimized heart disease prediction model using a meta-heuristic feature selection with improved binary salp swarm

M Sowmiya¹, B Banu Rekha², E Malar³

¹Department of ECE, PSG Institute of Technology and Applied Research, Coimbatore, 641062, India.

Computers in Biology and Medicine

|April 15, 2025

Summary

This summary is machine-generated.

This study introduces an advanced heart disease prediction model using a stacking classifier and an optimized meta-heuristic algorithm for accurate feature selection. The novel approach achieves high accuracy, aiding early detection and intervention for cardiovascular diseases.

Keywords:

Heart disease Meta-heuristic feature selection Predictive modelling Salp swarm algorithm Stacking classifier

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Biomedical Engineering
Artificial Intelligence in Healthcare
Computational Biology

Background:

Heart disease remains a significant global health burden despite technological progress.
Accurate predictive models are crucial for early detection and timely intervention.
Existing methods require enhancement for improved diagnostic accuracy.

Purpose of the Study:

To develop an accurate heart disease prediction model.
To integrate a stacking classifier with a nature-inspired meta-heuristic algorithm.
To optimize feature selection for enhanced predictive performance.

Main Methods:

An improved Binary Salp Swarm Algorithm (BSSA) with wolf optimizer and opposition-based learning was employed for optimal feature selection.
A two-tier Stacking Classifier (SC) architecture was utilized, with base classifiers at level 0 and a meta-learner at level 1.
A multi-objective strategy was adopted for feature selection to boost classification accuracy.

Main Results:

The proposed model achieved 95% accuracy, 0.92 sensitivity, 0.97 specificity, 0.96 precision, and a 0.95 F1 score on experimental datasets.
The model demonstrated low false positive and false negative rates.
Validation on larger datasets resulted in 87.46% accuracy, indicating robust performance.

Conclusions:

The developed heart disease prediction model shows superior performance compared to conventional techniques.
The integration of BSSA for feature selection and SC for classification significantly enhances predictive accuracy.
This approach holds substantial potential for improving clinical diagnosis of heart diseases.