Why the study?
Heart disease is a common deadly condition, and machine learning algorithms may enable its detection in a short time and at low cost.
Do machine learning models accurately predict heart disease using standard clinical datasets?
Do machine learning models accurately predict heart disease using standard clinical datasets?
Machine learning algorithms, particularly KNN and AdaBoost, demonstrate very high accuracy in classifying heart disease from standard datasets, potentially aiding clinical diagnosis.
May support ML-based CHD prediction tools; leaves open prospective validation before clinical adoption.
The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.
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Absar et al. (2022) studied Heart disease (n=1,025). Machine learning models (Random Forest, Decision Tree, AdaBoost, K-Nearest Neighbor) was evaluated on Prediction accuracy.
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