Background and Objectives: Taiwan is among the nations with the highest rates of Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN). As more cases are reported each year, there is a rise in hospital admissions for people seeking medical attention. This creates a burden on hospitals and affects the overall management and administration of the hospitals. Hence, this study aimed to develop a machine learning (ML) model to predict the Length of Stay (LoS) and mortality among T2DM and HTN inpatients. Materials and Methods: Using Taiwan’s National Health Insurance Research Database (NHIRD), this cohort study consisted of 58,618 patients, where 25,868 had T2DM, 32,750 had HTN, and 6419 had both T2DM and HTN. We analyzed the data with different machine learning models for the prediction of LoS and mortality. The evaluation was done by plotting descriptive statistical graphs, feature importance, precision-recall curve, accuracy plots, and AUC. The training and testing data were set at a ratio of 8:2 before applying ML algorithms. Results: XGBoost showed the best performance in predicting LoS (R2 0.633; RMSE 0.386; MAE 0.123), and RF resulted in a slightly lower performance (R2 0.591; RMSE 0.401; MAE 0.027). Logistic Regression (LoR) performed the best in predicting mortality (CV Score 0.9779; Test Score 0.9728; Precision 0.9432; Recall 0.9786; AUC 0.97 and AUPR 0.93), closely followed by Ridge Classifier (CV Score 0.9736; Test Score 0.9692; Precision 0.9312; Recall 0.9463; AUC 0.94 and AUPR 0.89). Conclusions: We developed a robust prediction model for LoS and mortality of T2DM and HTN inpatients. Linear Regression showed the best performance for LoS, and Logistic Regression performed the best in predicting mortality. The results showed that ML algorithms can not only help healthcare professionals in data-driven decision-making but can also facilitate early intervention and resource planning.

Patients at risk of chronic kidney disease (CKD) must be identified early and precisely in order to prevent complications, save lives, and limit expenditures for patients and health systems. This study aimed to develop a simple, high-precision machine learning model to identify individuals at risk of developing CKD in the near future, using a novel metabolic index with or without creatinine. This retrospective cohort study used data from the MJ medical record database collected between 2001 and 2015 in Taiwan. We used Cox hazard regression to identify potential predictors, including the novel metabolic index, for use as variables in the models. To develop a machine learning-based CKD risk model with fewer variables, we performed several experimental analyses to combine interacting variables into subsets. Those subsets were used to train three models, random forest, logistic regression, and XGBoost, with or without adding creatinine. The study included 12,189 participants, 20% with and 80% without CKD. The most important conventional predictors of CKD are age and gender. The novel metabolic index, TyG-Index, TG/HDL-ratio and VAI, had stronger predictive power than the conventional risk factors. Without including creatinine data, the XGBoost provided the best predictive performance. After adding creatinine, the performance of all the models was excellent, outperforming both conventional indicators and existing clinical algorithms for CKD. Using novel metabolic index in machine learning-based CKD risk prediction can accurately identify individuals at risk of diagnosis with CKD in the next year, with or without including creatinine.