Application of Random Forest for The Classification Diabetes Mellitus Disease in RSUP Dr. M. Jamil Padang

Abstract

Diabetes Mellitus is a disease in which blood sugar levels go beyond normal (GDS>200 mg/dl). Diabetes Mellitus may be defined as an insulin function disorder in the pancreatic organ. Diabetes Mellitus is a world health problem as incidents of this disease are increasing in every part of the world, including Indonesia. Prevention and control of the disease need to be made so as not to cause complications in other organs even to death. Because of this, one needs to study a method to predict the occurance of this disease and to knows the variable that most affect a person suffered from it. This could be accomplished by using a classification methods. One of classification methods is Random Forest. In this case study using randomForest packages in RStudio software. In general, the result of this study are the smallest OOB’s error rates (%) and Variable Importance Measure (VIM) using Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) values.The classification by a Random Forest methods on the incidence of Diabetes Mellitus in RSUP Dr. M. Jamil Padang results in OOB’s error rate was 1,2% or accuracy rates was 98,8%. The most optimal model produced using mtry = 4 and ntree = 1000. If used MDA, the variables that most affect are Age, Polyphagia, Polyuria, HB, and BMI. While if used MDG, the variables that most affect are Age, Polyphagia, BMI, HB, and Delayed Healing.