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

Barsasella, Diana and Bah, Karamo and Mishra, Pratik and Uddin, Mohy and Dhar, Eshita and Suryani, Dewi Lena and Setiadi, Dedi and Masturoh, Imas and Sugiarti, Ida and Jonnagaddala, Jitendra and Abdul, Shabbir Syed A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients. Medicina (Kaunas, Lithuania), 58. pp. 1-27. ISSN 1648-9144

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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

Item Type:	Article
Contributors:	Contribution Contributors NIDN/NIDK Email UNSPECIFIED Barsasella, Diana UNSPECIFIED UNSPECIFIED UNSPECIFIED Bah, Karamo UNSPECIFIED UNSPECIFIED UNSPECIFIED Mishra, Pratik UNSPECIFIED UNSPECIFIED UNSPECIFIED Uddin, Mohy UNSPECIFIED UNSPECIFIED UNSPECIFIED Dhar, Eshita UNSPECIFIED UNSPECIFIED UNSPECIFIED Suryani, Dewi Lena UNSPECIFIED UNSPECIFIED UNSPECIFIED Setiadi, Dedi UNSPECIFIED UNSPECIFIED UNSPECIFIED Masturoh, Imas UNSPECIFIED UNSPECIFIED UNSPECIFIED Sugiarti, Ida UNSPECIFIED ida.sugiarti@dosen.poltekkestasikmalaya.ac.id UNSPECIFIED Jonnagaddala, Jitendra UNSPECIFIED UNSPECIFIED UNSPECIFIED Abdul, Shabbir Syed UNSPECIFIED UNSPECIFIED
Uncontrolled Keywords:	predictive modeling; external validation; length of stay; mortality; type 2 diabetes; hypertension; machine learning
Subjects:	R Medicine > R Medicine (General)
Divisions:	Jurusan RMIK > D3 Rekam Medis & Informasi Kesehatan
Depositing User:	Ida Sugiarti
Date Deposited:	05 Jun 2023 04:59
Last Modified:	05 Jun 2023 04:59
URI:	http://repo.poltekkestasikmalaya.ac.id/id/eprint/1724

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