Abstract: Healthcare is an inescapable task to be done in human life. Cardiovascular disease is a broad category for a range of diseases that are affecting the heart and blood vessels. The early methods of forecasting cardiovascular diseases helped in making decisions about the changes to have occurred in high-risk patients which resulted in the reduction of their risks [5]. The health care industry contains lots of medical data, therefore machine learning algorithms are required to make decisions effectively in the prediction of heart diseases [4]. Recent research has delved into uniting these techniques to provide hybrid machine learning algorithms [5]. In the proposed research, data pre-processing uses techniques like the removal of noisy data, removal of missing data, filling default values if applicable, and classification of attributes for prediction [5] and decision making at different levels. The performance of the diagnosis model is obtained by using methods like classification, accuracy, sensitivity, and specificity analysis [5]. This project proposes a prediction model to predict whether people have heart disease or not and to provide awareness or diagnosis on the same [1]. This is done by comparing the accuracies of applying rules to the individual results of Support Vector Machine, KNN classifier, Decision Tree Classifiers, and logistic regression on the dataset [1] taken to present an accurate model of predicting cardiovascular disease.

Keywords: Heart Diseases; Machine Learning; Support Vector Machines; Decision Tree Classifier; KNN Classifier; Logistic Regression; Model Interpretation.

PDF | DOI: 10.17148/IJARCCE.2021.101223

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