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

Abstract

A collection of metabolic conditions known as diabetes mellitus are defined by hyperglycemia brought on by deficiencies in insulin secretion, action, or both. In terms of mortality rate, type-2 diabetes is 20 times higher when compared with type-1. Based on the earlier research, there is still scope to identify different risk levels of type-2 diabetes complications. To achieve this, we have proposed a T2DC machine learning-based prediction system using a decision tree as a base estimator with random forest to identify the severity of T2-DM complications at an early stage. Our proposed model achieved accuracies of 95.43%, 94.62%, 96.25%, 97.55%, and 97.83% for Nephropathy, Neuropathy, Retinopathy, Cardiovascularand Peripheral Vascular complications in T2-DM patients. The proposed model has the potential to improve clinical outcomes by promoting the delivery of early and personalized care to T2-DM patients.

An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis

“…Besides, recent advances in medical diagnosis have witnessed a surge in the adoption of deep learning methods. Studies such as (Ahmad, Ai, et al, 2019; Ahmad, Ding, et al, 2019; Alzghoul et al, 2023; Bataineh, 2019; Doppala et al, 2023; Furqan Qadri et al, 2019; Habib & Qureshi, 2023; Hirra et al, 2021; Qadri et al, 2019; Qadri et al, 2021; Qadri et al, 2022; Qadri et al, 2023; Vamsi et al, 2022) have demonstrated deep learning ability in dealing with large datasets and complex patterns. While deep learning methods provide robust performance (Ahmadian et al, 2021; Arora et al, 2022; Jalali, Ahmadian, Ahmadian, et al, 2022; Jalali, Arora, Panigrahi, et al, 2022; Jalali, Osorio, Ahmadian, et al, 2022; Mehnatkesh et al, 2023; Saffari et al, 2023), their computational density and the need for extensive data and training can be limited in certain scenarios.…”