Key result
Gradient Boosting model predicts cardiovascular disease with ~73% accuracy, outperforming other machine learning algorithms.
Why the study?
Early recognition of heart disease is important to reduce mortality, and machine learning can facilitate prediction using health data.
Do machine learning models accurately predict heart disease using clinical and lifestyle data?
Do machine learning models accurately predict heart disease using clinical and lifestyle data?
Machine learning models, particularly Gradient Boosting, can predict heart disease with moderate accuracy (73.48%) using basic clinical and lifestyle data.
Should not yet change clinical risk assessment; hypothesis-generating for ML models in CVD prediction and requires prospective validation.
Cardiovascular disease is the number one killer disease worldwide that causes up to a third of deaths recorded every year. In 2022, it has claimed 19.8 million life in the Eastern Europe alone with mortality rates being equally or higher in the United States, Southeast Asia, and India. To minimize these figures, early recognition is important, and machine learning (ML) can facilitate it using abundant amounts of health data. In this paper, the researcher investigates how to predict heart disease using five ML models and a data set of 70,000 registered patients. The data had 13 clinical features and lifestyle elements like age, cholesterol, blood pressure, glucose, smoking, and alcohol consumption. The dataset was prepared after data pre-processing that is, feature selection, and normalization prior to the training and test of the model on a 70:30 data split. Gradient Boosting model also produced the highest performance accuracy of 73.48%, precision of 0.7637, recall of 0.690 and the F1-score of 0.7249. These were supported by results of confusion matrix that saw high values of true positive and true negative. This paper has shown that ML models, especially Gradient Boosting model, can greatly assist in the early detection of heart diseases in patients, which suggests their use in the clinical diagnostics area provided that the models are further modified and tested.
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Das et al. (2025) studied Cardiovascular disease (n=70,000). Gradient Boosting model vs. Other machine learning models (Logistic Regression, Random Forest, Decision Tree, XGBoost) was evaluated on Model accuracy in predicting cardiovascular disease. The Gradient Boosting machine learning model achieved the highest performance in predicting cardiovascular disease with an accuracy of 73.48%, precision of 0.7637, and F1-score of 0.7249.
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