A Comparative Study for Prediction of Heart Diseases Using Machine Learning
7 Pages Posted: 4 Feb 2020
Date Written: January 28, 2020
According to the World Health Organization, heart diseases are the leading causes of death all over the world. Among the heart diseases, Heart Attacks are the major causes of death. The prediction or detection of the presence of heart disease is a task that requires a high level of expertise in the related field. However, with the advancement of technological research in disciplines like Machine Learning and Deep Learning, a lot of paths have opened up in the direction of getting this task done more easily and efficiently. One of these paths led to the automation of this task. However, while automating the system, we have to keep the system accurate. Apart from the quality of data, the choice of algorithm for solving the problem also plays an important role in producing accurate output. This is because different algorithms can perform differently with different kinds of data. The problem at hand can be solved by more than one kind of algorithm. The goal is to apply various machine learning algorithms to the problem and make a comparative study on the efficiency of these algorithms in predicting the presence of heart disease in a person. The algorithms used for the same are Support Vector Classifier, Decision Tree Classifier, Random Forest Classifier, and k Nearest Neighbour Classifier. The implementation of algorithms is done on the heart disease dataset from the University of California Irvine machine learning repository. The accuracy scores for all the algorithms mentioned above are to be evaluated. The algorithm with the highest accuracy score will be the most suitable one for predicting the disease.
Keywords: Machine learning (ML), Support Vector Machines (SVM), Supervised Learning, Heart Disease Prediction, k-Nearest Neighbour (kNN)
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