Machine learning applications in diabetes and hypertension mHealth systems primarily focus on disease prediction and diagnosis, with random forest frequently emerging as the best performing algorithm.
This review highlights that machine learning in diabetes and hypertension mHealth systems is primarily focused on prediction and diagnosis, with random forest and SVM showing the best performance, while therapeutic applications remain under-explored.
Abstract Mobile health (mHealth) systems are sipping into more and more healthcare functions with self-management being the foremost modus operandi. However, there has been challenges. This study explores challenges with mHealth self-management of diabetes and hypertension, two of the most comorbid chronic diseases. Existing literature present the challenges in fragments, certain subsets of the challenges at a time. Nevertheless, feedback from patient/users in extant literature depict very variegated concerns that are also interdependent. This work pursues provision of an encyclopedic, but not redundant, view of the challenges with mHealth systems for self-management of diabetes and hypertension. Furthermore, the work identifies machine learning (ML) and self-management approaches as potential drivers of potency of diabetes and hypertension mobile health systems. The nexus between ML and diabetes and hypertension mHealth systems was found to be under-explored. For ML contributions to management of diabetes, we found that machine learning has been applied most to diabetes prediction followed by diagnosis, with therapy in distant third. For diabetes therapy research, only physical and dietary therapy were emphasized in reviewed literature. The four most considered performance metrics were accuracy, ROC-AUC, sensitivity, and specificity. Random forest was the best performing algorithm across all metrics, for all purposes covered in the literature. For hypertension, in descending order, hypertension prediction, prediction of risk factors, and prediction of prehypertension were most considered areas of hypertension management witnessing application of machine learning. SVM averaged best ML algorithm in accuracy and sensitivity, while random forest averaged best performing in specificity and ROC-AUC.
Stephen et al. (Tue,) conducted a review in Diabetes and hypertension. MobileHealth systems and Machine Learning was evaluated. Machine learning applications in diabetes and hypertension mHealth systems primarily focus on disease prediction and diagnosis, with random forest frequently emerging as the best performing algorithm.