Diagnosis of Stroke and Diabetes Mellitus With Classification Techniques Using Decision Tree Method

Mudafiq Riyan Pratama (Politeknik Negeri Jember)
Arinda Lironika Suryana (Politeknik Negeri Jember)
Gamasiano Alfiansyah (Politeknik Negeri Jember)
Zora Olivia (Politeknik Negeri Jember)
Ida Nurmawati (Politeknik Negeri Jember)
Prawidya Destarianto (Politeknik Negeri Jember)

International Journal of Health and Information System (IJHIS)
Vol. 2 No. 1 (2024): May

: Stroke is a cerebral vascular disease characterized by the death of brain tissue that occurs due to reduced blood and oxygen flow to the brain. Ischemic stroke is associated with diabetes mellitus, therefore it is important to identify the risk factors that cause stroke and DM by diagnostic cause of the disease. This study aimed to classify and compare accuracy tests on medical record data sets for stroke and DM. This study analyzed the diagnosis of stroke and DM using Decision Tree. The risk factors consisted of gender, age, blood pressure, nutritional status, smoking, history of DM, and history of hypertension. The results of the analysis using the Decision Tree method showed that the accuracy rate was 86.67%, which means that the modeling has a good level of correctness of the prediction results. We conclude that the Decision Tree method was an accurate method for detecting stroke and DM.