Machine Learning for Screening Microvascular Complications in Type 2 Diabetic Patients Using Demographic, Clinical, and Laboratory Profiles

Abstract

Microvascular complications are one of the key causes of mortality among type 2 diabetic patients. This study was sought to investigate the use of a novel machine learning approach for predicting these complications using only the patient demographic, clinical, and laboratory profiles. A total of 96 Bangladeshi participants with type 2 diabetes were recruited during their routine hospital visits. All patient profiles were assessed by using a chi-squared (χ2) test to statistically determine the most important markers in predicting three microvascular complications: cardiac autonomic neuropathy (CAN), diabetic peripheral neuropathy (DPN), and diabetic retinopathy (RET). A machine learning approach based on logistic regression, random forest (RF), and support vector machine (SVM) algorithms was then developed to ensure automated clinical testing for microvascular complications in diabetic patients. The highest prediction accuracies were obtained by RF using diastolic blood pressure, albumin–creatinine ratio, and gender for CAN testing (98.67%); microalbuminuria, smoking history, and hemoglobin A1C for DPN testing (67.78%); and hemoglobin A1C, microalbuminuria, and smoking history for RET testing (84.38%). This study suggests machine learning as a promising automated tool for predicting microvascular complications in diabetic patients using their profiles, which could help prevent those patients from further microvascular complications leading to early death.

Status and Trends of the Association Between Diabetic Nephropathy and Diabetic Retinopathy From 2000 to 2021: Bibliometric and Visual Analysis

“…In patients with type 1 diabetes, atherosclerosis is associated with DR, and patients with DN have a greater coronary plaque burden than those with normoalbuminuria ( Kim et al, 2007 ; Lovshin et al, 2018 ). Next, cluster #5 was “machine learning.” It has been found that artificial intelligence (AI)-based machine learning can predict microvascular complications in diabetic patients ( Sambyal et al, 2021 ; Rashid et al, 2022 ).…”

Prediction of Micro Vascular and Macro Vascular Complications in Type-2 Diabetic Patients using Machine Learning Techniques

“…The significant observations show that the advanced structure with ML classifiers achieved an AUC ranging from 0.79 to 0.91. Rashid, M. et al [15] identified a root cause of death among T2-DM patients due to micro vascular problems. Their study aims to examine the use of the entire ML procedure in identifying issues using people's medical, clinical, and statistical examinations.…”

Clinical Research on Type 2 Diabetes: A Promising and Multifaceted Landscape

“…In this Special Issue, entitled “ Clinical Research on Type 2 Diabetes and Its Complications ” and published in the Journal of Clinical Medicine ( ), some valuable digital methodologies were used in different studies focusing on the type 2 diabetes syndrome. Novel machine learning techniques for predicting long-term complications are one of these approaches, as the studies of Huang, Rashid, and Shin et al depict [ 3 , 4 , 5 ]. The data presented by these authors suggest that machine learning may be more accurate in predicting diabetic microvascular complications than traditional methods.…”