Prediction Analysis Of Four Disease Risk Using Decision Tree C4.5

Technology has an important role in predicting a person's possible risk of chronic disease with the emergence of various kinds of research in the field of medical informatics, especially those related to the four chronic diseases that cause death in Indonesia, such as heart disease, stroke, cancer, and diabetes. Delays in medical treatment due to not predicting the disease can lead to other serious complications in the future. Therefore, doctors must think and work harder to assess patients early with factors influencing chronic disease risk. Many studies have carried out disease prediction focused only on one to two disease predictions. Still, research related to risk prediction for four diseases simultaneously has not been carried out in health and medical services. This study makes a risk prediction model for patients suffering from one or four chronic diseases, which goes through several stages in data mining from medical record data and assessments. We used 29 variables from two combination data sources, calculated using the C4.5 algorithm to predict the patient's risk for the disease. Based on the accuracy test results, the chronic disease prediction model has an accuracy value of 84%.This shows that the resulting model has good decision-making and high accuracy. For further research, other parameters can be used to predict disease, such as dietary habits in a certain period.