A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

Abstract

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels, and nerves. Methods: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications, mainly retinopathy, neuropathy, and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Results: It has been observed that statistical analysis can help only in inferential and descriptive analysis whereas, AI-based machine learning models can even provide actionable prediction models for faster and accurate diagnosis of complications associated with DM. Conclusion: The integration of AI-based analytics techniques, like machine learning and deep learning in clinical medicine, will result in improved disease management through faster disease detection and cost reduction for the treatment.