Abstract: Diabetes is a chronic illness with the potential to cause a worldwide health catastrophe. Diabetes affects 382 million people globally, according to the International Diabetes Federation. This headcount will have more than tripled to 592 million by 2035. The fundamental purpose of this study is to develop a prediction model based on the medical data provided by diabetic and non-diabetic individuals. The purpose of this study is to create a hybrid model that physicians may use to manage diabetic patients. To begin building the prediction model, key parameters such as Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, and Age were selected from the PIMA Indian Diabetes Dataset. The dataset was separated into two parts: training and testing. We then proceeded based on these findings. Following that, we utilised a random forest machine learning system to predict whether the patient will be normal (non-diabetic) or diabetic.

Keywords: Type 2 Diabetes, Machine Learning, Random Forest, Prediction.

PDF | DOI: 10.17148/IJARCCE.2022.115185

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