Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study

Abstract

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.

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

“…For the assessment of congenital heart disease, the RF-based framework's prediction accuracy is 97 percent [33], with a specificity of 88 percent and a sensitivity of 85 percent. With a specificity of 95% and a sensitivity of 93.5 percent, we used LR, EVF, MARS, and CART ML models in [34] to detect the co-occurrence of CVD and 94 percent.…”

Smartphone Apps for Diabetes Medication Adherence: Systematic Review

“…With the advancement in smartphone technology, various health vitals could be captured and transferred in real time for analysis [ 79 - 81 ]. The application of machine learning and big data could provide a wealth of predictive information to the patient and to health care professionals [ 82 - 87 ]. Furthermore, high-quality apps coupled with evidence-based ICT programs using user-centric designs, wearable device, and machine learning approaches could be used to provide personalized interventions for people with diabetes [ 84 , 87 - 93 ].…”

“…The application of machine learning and big data could provide a wealth of predictive information to the patient and to health care professionals [ 82 - 87 ]. Furthermore, high-quality apps coupled with evidence-based ICT programs using user-centric designs, wearable device, and machine learning approaches could be used to provide personalized interventions for people with diabetes [ 84 , 87 - 93 ]. Hence, our findings could have practical and research implications for diabetes medication adherence.…”

Consensus on Recommended Functions of a Smart Home System to Improve Self-Management Behaviors in People With Heart Failure: A Modified Delphi Approach

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