Abstract
Background: The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle – income countries calls for preventive public health interventions. Studies from Africa including those from Ghana, consistently reveal high T2DM-related mortality rates. While previous research in the Ho municipality has primarily examined risk factors, comorbidity, and quality of life of T2DM patients, this study specifically investigated mortality predictors among these patients.
Method: The study was retrospective involving medical records of T2DM patients. Data extracted were analyzed using Stata version 16.0 and Python 3.6.1 programming language. Both descriptive and inferential statistics were done to describe and build predictive models respectively. The performance of machine learning (ML) techniques such as support vector machine (SVM), decision tree, k nearest neighbor (kNN) and logistic regression were evaluated using the best-fitting predictive model of T2DM mortality.
Results: Out of the 328 participants, 183(55.79%) were females. An 11.28% mortality was recorded. A 100% mortality was recorded among the T2DM patients with sepsis (p-value = 0.012). T2DM patients were 3.83 times as likely to die [AOR = 3.83; 95% CI: (1.53-9.61)] if they had nephropathy compared to T2DM patients without nephropathy (p-value = 0.004). The full model which included sociodemographic characteristics, family history, lifestyle variables and complications of T2DM had the best prediction of T2DM mortality outcome (ROC = 72.97%). The accuracy for (test and train datasets) were as follows: (90% and 90%), (100% and 100%), (90% and 90%) and (90% and 88%) respectively for the various classification techniques: logistic regression, Decision tree classifier, kNN classifier and SVM.
Conclusion: This study found that all patients with sepsis died. Nephropathy was the identified significant predictor of T2DM mortality. Decision tree classifier provided the best classifying potential.