A Comparative Analysis and Statistical Inference of Diabetes Cases

The emergence of diabetes caused due to increased level of blood glucose has widely affected the health of mankind across the globe. This is caused due to insufficient production of insulin that causes glucose to enter into cells and produce energy. Also scarcity of insulin may lead to continuous circulation of glucose preventing it to enter the cells leading to diabetes. The most prevalent forms of the disease are type 1, type 2, and gestational diabetes. As per the analysis of National Institutes of Health (NIH) as per 2015, around 9.4% of the population have diabetes. Every fourth person over 65 has diabetes. Type 2 diabetes affects 90–95% of adult patients. The more concerning aspect is that 1 among 4 of them is ignorant about their disease. Considering the scenario, it is imperative to devise some method to predict the diabetes so that damage can be minimized. Authors in this paper aim to consider various crucial aspects like glucose, pregnancies, blood pressure, insulin, skin thickness, BMI, diabetes pedigree function, and age to predict the disease using different machine learning models, and the results advocate the efficiency of machine learning models for disease prediction.