Jerhi Wahyu Fernanda, Toha Saifudin, Nur Chamidah, Eva Firdayanti Bisono, Ratna Frenty Nurkhalim, Gangga Anuraga
The complexity of type 2 diabetes mellitus length of stay data increases the risk of outliers. Outliers negatively impact model performance in both classification and regression models. However, there is limited research combining Isolation Forest and Boxplot outlier detection with ensemble methods. This study aims to evaluate the combination of isolation Forest and Boxplot as outlier detection methods for improving extreme gradient boosting performance in predicting T2DM length of stay. The data used for the analysis consisted of 587 T2DM patients. Using two-step outlier detection with both isolation forest and boxplot, MAPE has decreased by 1.4%. XGBoost with Isolation Forest and Boxplot outlier detection outperformed the standalone XGBoost model and XGBoost with only isolation forest. In predicting complex data, two-step outlier detection provides better result than using a single outlier detection methods © 2025 IEEE.
Airlangga University, Doctoral Programme of Mathematics, Surabaya, Indonesia; Airlangga University, Departement of Mathematics, Surabaya, Indonesia; Institute of Health Sciences Bhakti Wiyata, Departement of Medical Record, Kediri, Indonesia; PGRI Adi Buana University, Departement of Statistics, Surabaya, Indonesia
Research at a Glance
Register to unlock
Topics & SDG Alignment
Register to unlock
Collaboration
Register to unlock
Author Profile (Selected)
Register to unlock
References Overview
Register to unlock
Journal & Source
Register to unlock
Metadata & Integrity
Register to unlock