Diabetes Prediction and Apprehension with Focus Both on Clinical and Non-Clinical Factors - Gupta, Aditya; Singh, Angad; Jani, Maharshi; Salaria, Yuvraj; Hiremani, Vani; Gonge, Sudhanshu; Kotecha, Ketan (International Journal of Intelligent Systems and Applications in Engineering, 2023)
An industrial revolution has changed the daily lifestyle of engineers, doctors and common people. This is due to a lot of experimental work performed and carried out in the food industry, IT sector, agriculture industry, automobile industry, etc. It has an impact on the diet of the stakeholders. Due to this the enzyme generation gets reduced and may lead to low production of insulin. As insulin is one of the important parts of the blood which controls all properties of plasma, water, enzymes, protein, vitamins and minerals, this causes diabetes to the patient. It is the essentially the most common and widespread chronic disease in the world. In our research paper both clinical and non-clinical parameters are considered such as Insulin, Glucose, BMI, smoking, stress, BP, Junk food etc. From this the aim of the research is to emphasize on both kinds of factors that affect a person’s probability of Diabetes detection. There are several techniques such as models of SVM, Decision Tree, Random Forest and Logistic Regression which were found useful for predicting and apprehending the features to identify diabetes. We have done comparative analysis of techniques to observe the output after applying clinical and non-clinical factors.