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2000
Volume 18, Issue 7
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Background

Detecting heart disease in a timely manner is vital for preventing its progression, as it is the primary cause of death across the globe. Machine learning has the potential to enhance diagnostic accuracy and enable better clinical decision-making. A machine learning-powered hybrid system for diagnosing heart disease may provide a better optimal solution for heart disease prediction

Objective

The overarching objectives include accuracy improvement, enhanced classification reliability, and the development of high-performance prediction models for heart disease. These objectives indicate a commitment to advancing methodologies and models in the field of machine learning and data science, particularly within the domain of healthcare and disease prediction.

Methods

The proposed system was developed using the Cleveland dataset that was preprocessed and analyzed using Recursive Feature Elimination with Cross-Validation (RFECV) and Least Absolute Shrinkage and Selection Operator (LASSO) feature extraction techniques. Further, a hybrid feature selection approach using RFECV and K-Best has been proposed for feature selection. Eight machine learning classifiers such as Multilayer Perceptron (MLP), Random Forest (RF), Extreme Gradient Boosting (XGB), K-Nearest Neighbours (KNN), Extra Tree (ET), Support Vector Machine (SVC), Adaboost, Decision Tree (DT) were utilized, and the performance of the system was measured in terms of various metrics.

Results

The results showed that the proposed HSLE algorithm with hybrid feature selection led to the highest overall accuracy of 98.76%.

Conclusion

As mentioned, the main cause of adult death worldwide is chronic disease. Early detection can stop the condition from getting worse. Our research presents an innovative hybrid machine-learning approach designed to forecast heart disease.

© 2025 Bentham Science Publishers
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2024-05-10
2025-08-18

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HSLE: A Hybrid Ensemble Classifier for Prediction of Heart Disease
Recent Advances in Electrical & Electronic Engineering 18, 781 (2025); https://doi.org/10.2174/0123520965291887240422051823
/content/journals/raeeng/10.2174/0123520965291887240422051823
/content/journals/raeeng/10.2174/0123520965291887240422051823
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  • Article Type:     Research Article
Keyword(s): Feature selection; heart disease; K-best; LASSO; RFECV; super learner ensemble

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