DIABETES MELLITUS PREDICTION AND DIAGNOSIS USING MACHINE LEARNING

Abstract

One chronic metabolic disease, which is now a major global public health issue is diabetes mellitus. It is caused by either insufficient insulin synthesis or poor insulin use by the body, and elevated blood sugar levels mostly mark it. Numerous factors, such as ageing, obesity, poor eating habits, sedentary lifestyles, and genetic susceptibility, affect its development. In order to reduce long-term effects like cardiovascular illnesses, kidney problems, and nerve damage, early detection of diabetes is essential. In capacity various machine learning methods are used to analyse complex medical information and aid in early diagnosis, including Pima Indians Diabetes, has garnered significant attention due to the rapid breakthroughs in data science. Clinical factors include blood pressure, blood sugar, age, BMI, and family history. Finding patterns and correlations in with conventional statistical methods may prove difficult.[5] Examples of these models are XGBoost, K-Nearest Neighbours (KNN), Random Forest, and Support Vector Machine (SVM).[9],[10],[11] The objective of the previously described project is to develop machine learning models for the identification of diabetes. Using open-source datasets by applying supervised machine learning methods. Keywords: Diabetes prediction, Machine learning, Classification, confusion matrix, healthcare, ROC curve and AUC, predictive modelling