Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
Ch Anwar Ul Hassan1, Jawaid Iqbal2, Rizwana Irfan3
1Department of Creative Technologies, Air University Islamabad, Islamabad 44000, Pakistan.
Insights
Machine learning models accurately predict heart disease risk. Random Forest achieved 96% accuracy, improving clinical decision-making for coronary heart disease prevention.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Coronary heart disease (CHD) is a leading global cause of mortality.
- Accurate prediction of CHD remains a significant challenge in clinical data analysis.
- Machine learning (ML) offers potential for diagnostic assistance and data-driven prediction in healthcare.
Purpose of the Study:
- To evaluate the effectiveness of eleven ML classifiers for heart disease prediction.
- To identify key features that enhance the predictability of CHD.
- To develop and assess ML models for improved CHD risk assessment.
Main Methods:
- Utilized eleven distinct ML classifiers for heart disease prediction.
- Employed various feature combinations to optimize prediction models.
- Applied well-known classification algorithms to clinical datasets.
Main Results:
- Achieved 95% accuracy using gradient boosted trees and multilayer perceptron models.
- Random Forest classifier demonstrated superior performance with 96% accuracy.
- Identified key features that significantly improved CHD prediction accuracy.
Conclusions:
- ML techniques, particularly Random Forest, are highly effective for CHD prediction.
- Feature selection and optimized ML models can substantially enhance diagnostic accuracy.
- ML-based prediction models show promise for clinical decision support in cardiology.
Abstract:
Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.
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