Machine Learning Algorithms for Heart Disease Prediction: A Comparative Analysis

Machine learning has grown in popularity as a result of the widespread application of its algorithms in numerous data science projects across many industries, particularly in the health-care industry. Machine learning technologies must be used to assist researchers and medical professionals in the early diagnosis of diseases such as heart disease, which is one of the world’s leading causes of death. Correct heart disease prediction can save lives and avoid health problems, but inaccurate heart disease prediction can be fatal. Machine learning algorithms excel at learning from data, and because healthcare providers collect massive amounts of data on a regular basis, these algorithms have a lot of room to grow in this industry. A comparative analytical technique was used in this research study to determine which algorithm works better under the specified conditions. Various tests were carried out using 5 and 10 fold cross validation to confirm that the models produced were sufficiently generalizable. The data for this study comes from a machine learning database at the University of California, Irvine (UCI), which contains 303 instances with 14 attributes. The obtained data is normalised using the Min-Max method. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Nave Bayes (NB), Random Forest (RF), and Gradient Boosting ensemble approach are some of the common models developed utilising supervised machine learning classification algorithms on scaled data. Standard performance criteria such as accuracy, recall, and F1-score are also used to evaluate these methods. Based on the results of the studies, it can be determined that SVM outperforms the other algorithms.

Author(S) Details

Isreal Ufumaka
Department of Computer Science, University of Benin, Nigeria.

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