Key result
Combining machine learning with clinical risk factors greatly improves early cardiac disease prediction.
Why the study?
Improving patient outcomes depends on early diagnosis of cardiac disease, but there is a paucity of study into predicting the early symptoms of cardiac disease using machine learning.
Do machine learning algorithms improve the prediction accuracy of heart disease?
Systematic Review
Do machine learning algorithms improve the prediction accuracy of heart disease?
Machine learning algorithms combined with clinical risk factors can significantly improve the prediction accuracy of early cardiac disease.
Answers are grounded in this paper and cite their sources.
May refine early cardiac risk stratification in practice; extends systematic evidence on ML-augmented models.
Machine learning (ML) is a fast growing topic of study. A rising number of studies use ML as a powerful theoretical framework. However, there is a paucity of study into predicting the early symptoms of cardiac disease. Improving patient outcomes depends on early diagnosis of cardiac disease. A comprehensive review of several ML algorithms for the prediction of heart disease is discussed in this work. Various methods were reviewed such as Naive Bayes classifiers, regression models (Logistic, Linear, Lasso), Decision trees, Support vector machines, Ensemble methods and Neural networks. The study evaluated the effect of prediction accuracy on Obesity., high blood pressure, diabetes, high cholesterol., alcohol consumption and smoking are important risk factors. These results show that prediction accuracy is greatly improved when ML algorithms are combined with different risk factors. The selection of suitable algorithms to enhance cardiac disease prediction and overall healthcare outcomes are discussed in this review.
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Netra et al. (2025) conducted a systematic review in Heart Disease. Machine learning algorithms was evaluated on Prediction accuracy of early symptoms of cardiac disease. Combining machine learning algorithms with risk factors such as obesity, high blood pressure, and diabetes greatly improves the prediction accuracy of early cardiac disease symptoms.
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