A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients

Abstract

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.

Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis

“…After evaluating the models' performance, we found that Ridge and XGBoost regressions outperformed the others, resulting in lower prediction errors. Our findings align with previous studies, such as Chen and Klasky (2022), which reported similar results with lower prediction errors or loss functions. For instance, they reported the lowest mean absolute error between prediction and actual duration to be around 4 days, while our study showed a similar result of around 6 days.…”

“…Adopting precise and accurate modeling techniques improves the results and interpretations. In recent years, the prediction of patient LoS for various diseases and scenarios has been extensively explored using a variety of statistical and machine learning methods such as Logistic Regression (LoR), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), decision tree-based methods, among others (Barsasella et al, 2022 ).…”

Optuna Based Hyperparameter Tuning for Improving the Performance Prediction Mortality and Hospital Length of Stay for Stroke Patients

“…These findings are strengthened by the correlation between the length of time a patient is treated in the ICU and the likelihood of death, especially if the patient spends more than three days in the intensive care unit (Barsasella et al, 2022). The results of this study align with other findings, showing that a longer duration of hospitalization is associated with higher mortality and readmission rates (Rachoin et al, 2020).…”

Hospitalization Length of Stay Prediction using Patient Event Sequences

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