Track: Healthcare Systems

Abstract

Heart disease is the leading cause of death in most countries. According to 2018 statistics, about 17.9 million people worldwide died from cardiovascular disease. Early prediction and control can help reduce mortality from heart disease using existing health data. The decision tree method accurately builds a computational model that aims to predict. The usefulness of decision trees in health has made them used to predict cancer, diagnose lung disease, diagnose heart disease, etc. More studies have also shown that geographical variables can affect the prevalence of hypertension, one of the causes of heart disease. Although hypertension is closely related to geographic variables and has a relationship with heart disease, there are still not many studies related to heart disease prevention and have not used the geographic location as a variable. This study predicts the risk of heart disease by combining 15 variables of heart disease patient data obtained from Azra Hospital, Indonesia and geographic data in the form of an area's altitude. We have categorized the elevation data from 367 villages in West Java into 2 categories based on the indicators of plains in Indonesia, highlands (>600m) and lowlands (<600m). Based on the test results, we found that the decision tree method's diagnosis had an accuracy of  93,75%, with the highest level of risk being in people living in the lowlands. This shows that the altitude of an area can affect the risk of heart disease. We suggest optimizing the method to improve the accuracy of the prediction results.