Abstract: People are undergoing a routine and busy schedule that leads to stress and anxiety. In addition to this, the percentage of people who are obese, stressed, and addicted to cigarettes is going up drastically [4]. This is leading to heart diseases. Heart diseases are one of the utmost causes of death in the world. The number of people affected by heart disease increases irrespective of age in both men and women [4]. The challenge behind these diseases is their timely prediction. While factors like gender, diabetes, and BMI also contribute to this disease, the chances of having heart disease also increase with the age. Men have a greater risk of heart disease. However, women also have the same possibility after menopause. Leading a stressed life can increase the chance of coronary heart disease.
In the proposed research, to pre-process data we’ve used techniques like the removal of noisy data, removal of missing data, filling default values if applicable, and classification of attributes for prediction and decision making at different levels. The performance of the diagnosis model is obtained by using methods like classification, accuracy, sensitivity, and specificity analysis [16]. This project proposes a prediction model to predict whether people have heart disease or not and to provide awareness or diagnosis on the same [16]. 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 taken to present an accurate model of predicting cardiovascular disease.
Keywords: Coronary Heart Disease; Decision Tree Classifier; K Nearest Neighbor; Machine Learning; Naive Bayes; Support Vector Machine
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DOI:
10.17148/IJARCCE.2022.114206
