Classification and prediction of heart disease using Machine Learning models: A promising approach for medical diagnosis

Abstract

Background: heart disease is one of the leading causes of death worldwide, claiming 17.9 million lives. They are a major public health problem that affects people regardless of age or gender. Objective: This work aims to classify and predict heart disease using Machine Learning (ML) models such as Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) and Logistic Regression (LR). Methods: We worked with the Cleveland dataset from Kaggle, consisting of 303 patient records and 14 attributes. This research was conducted in different stages, including model understanding, dataset analysis and cleaning, ML model training, and model performance evaluation. Results: The results showed that the RF and KNN models achieved the highest levels of performance and accuracy with 88.52%, surpassing the other models such as SVM, NB, and LR which obtained 86.89% accuracy, and DT with 78.69%. Conclusions: In conclusion, the RF and KNN models stand out over the other models for this type of prediction task.