Missing Value Imputation in Data MCAR for Classification of Type 2 Diabetes Mellitus and its Complications

Type 2 diabetes mellitus (T2DM) is a disease that is at risk for many complications. Previous research on the prognosis of T2DM and its complications is limited to the impact of T2DM on one particular disease. Guidebook for T2DM Management in Indonesia has eight categories of T2DM complications. The purpose of this study is to classify T2DM prognosis into eight categories: one controlled class and seven classes of aggravating disorders. The classification was based on medical record data from T2DM patients at Panti Rapih Hospital in Yogyakarta between 2017 and 2022. The problem is that the medical record data has numerous missing values (MV). The dataset had 29% missing values, classified as Missing Completely at Random (MCAR). This study performed imputation on the dataset prior to categorization. For MV imputation, a variety of imputation methods were used, and their accuracy was measured using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The best imputation results were utilized to update the dataset. Subsequently, the dataset was used for classification employing several classification methods. The classification results were compared to determine the method with the highest accuracy in this scenario. The Decision Tree method with stratified k-fold cross-validation emerged as the optimal method for this classification. The results revealed an average accuracy value of 0.8529.