Key result
Random Forest outperforms other machine learning methods for heart disease prediction with 99% accuracy.
Why the study?
Diagnosing heart disease is difficult because it entails past health history, and healthcare data is often not extracted effectively to uncover hidden facts for decision-making.
Do machine learning methods accurately predict heart disease?
Do machine learning methods accurately predict heart disease?
Random Forest ensemble techniques can predict heart disease with up to 99% accuracy, demonstrating the potential of machine learning in automated cardiac diagnosis.
Does not support clinical adoption yet; leaves open prospective validation of ML models in diverse heart disease cohorts.
Healthcare industry generates a vast amount of data, the majority of which is sophisticated and massive in size. This information, however, is not "extracted" in order to uncover hidden facts for effective decision-making. Diagnosing heart disease, a noncommunicable disease, is one of the more difficult problems in medicine because it entails the patient's past health history. An accurate and effective automated system can be quite beneficial in detecting cardiac problems. Modern data mining techniques may be able to solve this issue. In the healthcare industry, various information-extracting technologies such as association rule mining, classification, and clustering are used to forecast cardiac disease. In order to address this issue, the research investigates the use of machine learning methods for cardiac disease prediction, including Naive Bayes (NB), logarithmic regression (LR), Support Vector Machine (SVM), the Decision Tree (DT), Random Forests (RF), and the k-nearest-neighbor algorithm (KNN). The Random Forest ensemble technique outperforms with 99% accuracy.
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Mahesh et al. (2024) studied Heart disease. Random Forest ensemble technique vs. Other machine learning methods (NB, LR, SVM, DT, KNN) was evaluated on Prediction accuracy. The Random Forest ensemble technique outperformed other machine learning methods for predicting heart disease, achieving 99% accuracy.
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