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Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective
Ahmad Shaker Abdalrada1,2, Jemal Abawajy2, Tahsien Al-Quraishi1,2
1Faculty of Computer Science and Information Technology, Wasit University, Al Kut, Iraq.
Insights
This study developed a machine learning model to predict the co-occurrence of diabetes mellitus (DM) and cardiovascular diseases (CVD). The model achieved high accuracy, aiding early detection and preventive treatment for these common conditions.
Area of Science:
- Biomedical Informatics
- Machine Learning in Healthcare
- Public Health
Background:
- Diabetic mellitus (DM) and cardiovascular diseases (CVD) represent a significant global health burden, frequently co-occurring.
- Current diagnostic approaches often miss individuals with co-existing DM and CVD, leading to delayed treatment and increased complications.
- Early identification of DM and CVD co-occurrence is crucial for effective management and reducing morbidity.
Purpose of the Study:
- To develop and evaluate a two-stage machine learning (ML) model for predicting the co-occurrence of DM and CVD.
- To identify significant common risk factors associated with the co-existence of DM and CVD.
- To improve early detection rates for individuals at risk of both conditions.
Main Methods:
- Utilized the diabetes complications screening research initiative (DiScRi) dataset (>200 variables, >2000 participants).
- Employed a two-stage ML approach: Stage 1 used logistic regression and Evimp functions within a multivariate adaptive regression splines model to identify common risk factors, followed by correlation matrix analysis for redundancy reduction.
- Stage 2 involved developing a classification and regression algorithm, with model performance evaluated using prediction accuracy, sensitivity, and specificity.
Main Results:
- Identified key risk factors for DM and CVD co-occurrence: family history, gender, heart rate variability (deep breathing, lying to standing), HbA1c, HDL, and TC/HDL ratio.
- The predictive model indicated a 97.9% probability of co-occurrence for participants with HbA1c >6.45 and TC/HDL ratio >5.5.
- Achieved a high overall predictive accuracy of 94.09%, with 93.5% sensitivity and 95.8% specificity for detecting DM and CVD co-occurrence.
Conclusions:
- The developed ML model demonstrates high accuracy in predicting DM and CVD co-occurrence in screening program attendees.
- This predictive capability can facilitate early identification of at-risk individuals, enabling timely preventive interventions.
- The model holds potential for reducing the future healthcare burden associated with DM and CVD.
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.
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