Key result
Machine learning models detect chronic ischemic heart disease with up to 99% accuracy.
Why the study?
Coronary heart disease is a major cause of mortality in Pakistan, and previous models tried with different attributes failed to enhance detection accuracy.
Do machine learning methods (LR, NB, RF) provide high detection accuracy for chronic ischemic heart disease?
Do machine learning methods (LR, NB, RF) provide high detection accuracy for chronic ischemic heart disease?
Machine learning models, particularly Random Forest, can diagnose chronic ischemic heart disease with very high accuracy.
Answers are grounded in this paper and cite their sources.
May support ML-aided diagnosis of chronic ischemic heart disease; leaves open prospective clinical validation.
Ischemic heart disease (IHD) causes discomfort or irritation in the chest. According to the World Health Organization, coronary heart disease is the major cause of mortality in Pakistan. Accurate model with the highest precision is necessary to avoid fatalities. Previously several models are tried with different attributes to enhance the detection accuracy but failed to do so. In this research study, an artificial approach to categorize the current stage of heart disease is carried out. Our model predicts a precise diagnosis of chronic diseases. The system is trained using a training dataset and then tested using a test dataset. Machine learning methods such as LR, NB, and RF are applied to forecast the development of a disease. Experimental outcomes of this research study have proven that our strategy has excelled other procedures with maximum accuracy of 99 percent for RF, 97 percent for NB, and 98 percent for LR. With such high accuracy, the number of deaths per year of ischemic heart disease will be slightly decreased.
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Shehzadi et al. (2022) studied Chronic Ischemic Heart Disease. Machine learning methods (Random Forest, Naive Bayes, Logistic Regression) vs. Other procedures was evaluated on Diagnostic accuracy. Machine learning methods accurately diagnosed chronic ischemic heart disease, with Random Forest achieving a maximum accuracy of 99%, Logistic Regression 98%, and Naive Bayes 97%.
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