Heart Disease Prediction Using Principal Component Analysis and Decision Tree Algorithm - Hambali, Moshood Abiola; Gbolagade, Morufat Damola; Olasupo, Yinusa Ademola (ICSE (Institute of Computer Sciences and Engineering), 2023)
Globally, cardiovascular disease is among the major diseases that lead to death. Early forecasts are crucial. Using the patient's medical record, the supervised learning algorithm for predicting heart disease at an early stage was proposed. The principal component analysis (PCA) classifier and decision tree algorithm were created to classify medical record data. To predict cardiovascular diseases, data mining was utilized. The proposed strategy improves the diagnostic efficiency of physicians. Using data received from the UCI repository, the classifier's efficacy was confirmed. PCA offers 98% precision, 100% sensitivity, and 98% accuracy. In terms of accuracy, sensitivity, and precision, the results showed that the PCA outperformed the decision tree.