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A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients
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 t...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694021/ https://www.ncbi.nlm.nih.gov/pubmed/36363525 http://dx.doi.org/10.3390/medicina58111568 |
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author | Barsasella, Diana Bah, Karamo Mishra, Pratik Uddin, Mohy Dhar, Eshita Suryani, Dewi Lena Setiadi, Dedi Masturoh, Imas Sugiarti, Ida Jonnagaddala, Jitendra Syed-Abdul, Shabbir |
author_facet | Barsasella, Diana Bah, Karamo Mishra, Pratik Uddin, Mohy Dhar, Eshita Suryani, Dewi Lena Setiadi, Dedi Masturoh, Imas Sugiarti, Ida Jonnagaddala, Jitendra Syed-Abdul, Shabbir |
author_sort | Barsasella, Diana |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9694021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96940212022-11-26 A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients Barsasella, Diana Bah, Karamo Mishra, Pratik Uddin, Mohy Dhar, Eshita Suryani, Dewi Lena Setiadi, Dedi Masturoh, Imas Sugiarti, Ida Jonnagaddala, Jitendra Syed-Abdul, Shabbir Medicina (Kaunas) Article 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. MDPI 2022-10-31 /pmc/articles/PMC9694021/ /pubmed/36363525 http://dx.doi.org/10.3390/medicina58111568 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Barsasella, Diana Bah, Karamo Mishra, Pratik Uddin, Mohy Dhar, Eshita Suryani, Dewi Lena Setiadi, Dedi Masturoh, Imas Sugiarti, Ida Jonnagaddala, Jitendra Syed-Abdul, Shabbir A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients |
title | A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients |
title_full | A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients |
title_fullStr | A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients |
title_full_unstemmed | A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients |
title_short | A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients |
title_sort | machine learning model to predict length of stay and mortality among diabetes and hypertension inpatients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694021/ https://www.ncbi.nlm.nih.gov/pubmed/36363525 http://dx.doi.org/10.3390/medicina58111568 |
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