Performance Testing of KNN and Logistic Regression Algorithms in Classifying Heart Disease Susceptibility

Abstract

The annual global death toll due to cardiovascular diseases, which fall into the category of heart and blood vessel disorders, reaches 17.9 million lives. This undoubtedly requires more attention in order to anticipate the potential risk of heart attacks that can affect anyone at any time. Data analysis or data mining approaches have become a significant contribution in the field of information technology to provide valuable information regarding the risk of heart diseases. Data analysis using the K-Nearest Neighbor and Logistic Regression algorithms is expected to provide information related to the susceptibility category for heart diseases, such as age susceptibility, gender, cholesterol levels, and so on. With the information obtained from this data analysis, it is hoped that it can serve as a reference and consideration for individuals to be more vigilant in maintaining their health. The results indicate that the highest correlation with susceptibility to heart disease is based on a person's age and their body weight. The correlation coefficient between these two variables is 0.37, suggesting a relationship between a person's age and their body weight, which can make them more susceptible to heart disease. Testing with both algorithms shows a high level of accuracy, with K-Nearest Neighbor achieving an accuracy rate of 0.95, while Logistic Regression has an accuracy of 0.96.