HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm

Abstract

Supervised machine learning algorithms are powerful classification techniques commonly used to build prediction models that help diagnose the disease early. However, some challenges like overfitting and underfitting need to be overcome while building the model. This paper introduces hybrid classifiers using the ensembled model with a majority voting technique to improve prediction accuracy. Furthermore, a proposed preprocessing technique and features selection based on a genetic algorithm is suggested to enhance prediction performance and overall time consumption. In addition, the 10-folds cross-validation technique is used to overcome the overfitting problem. Experiments were performed on a dataset for cardiovascular patients from the UCI Machine Learning Repository. Through a comparative analytical approach, the study results indicated that the proposed ensemble classifier model achieved a classification accuracy of 98.18% higher than the rest of the relevant developments in the study.

Keywords

• Cardiovascular disease
• supervised machine learning algorithms
• simple genetic algorithm
• ensembled model
• majority voting technique