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Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study
BACKGROUND: Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9167176/ https://www.ncbi.nlm.nih.gov/pubmed/35673486 http://dx.doi.org/10.1007/s40200-021-00968-z |
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author | Abdalrada, Ahmad Shaker Abawajy, Jemal Al-Quraishi, Tahsien Islam, Sheikh Mohammed Shariful |
author_facet | Abdalrada, Ahmad Shaker Abawajy, Jemal Al-Quraishi, Tahsien Islam, Sheikh Mohammed Shariful |
author_sort | Abdalrada, Ahmad Shaker |
collection | PubMed |
description | BACKGROUND: Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD. METHODS: We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics. RESULTS: Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio ≤ 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c ≤5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c ≤5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%. CONCLUSIONS: Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help in early detection of patients with DM and CVD who could benefit from preventive treatment and reduce future healthcare burden. |
format | Online Article Text |
id | pubmed-9167176 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-91671762022-06-06 Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study Abdalrada, Ahmad Shaker Abawajy, Jemal Al-Quraishi, Tahsien Islam, Sheikh Mohammed Shariful J Diabetes Metab Disord Research Article BACKGROUND: Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD. METHODS: We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics. RESULTS: Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio ≤ 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c ≤5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c ≤5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%. CONCLUSIONS: Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help in early detection of patients with DM and CVD who could benefit from preventive treatment and reduce future healthcare burden. Springer International Publishing 2022-01-12 /pmc/articles/PMC9167176/ /pubmed/35673486 http://dx.doi.org/10.1007/s40200-021-00968-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Abdalrada, Ahmad Shaker Abawajy, Jemal Al-Quraishi, Tahsien Islam, Sheikh Mohammed Shariful Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study |
title | Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study |
title_full | Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study |
title_fullStr | Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study |
title_full_unstemmed | Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study |
title_short | Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study |
title_sort | machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9167176/ https://www.ncbi.nlm.nih.gov/pubmed/35673486 http://dx.doi.org/10.1007/s40200-021-00968-z |
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