Diabetes Prediction Using Colab Notebook Based Machine Learning Methods

Abstract

Diabetes is getting more and more common around the world. People suffer from diabetes or live at risk associated with this disease. It is necessary to prevent health problems caused by diabetes, to reduce the risk of diabetes and to reduce a load of diabetes on the health system. Therefore, it is important to diagnose and treat diabetic patients early. In this study, Pima Indian Diabetes (PID) database was used to predict diabetes. Random Forest Classifier, Extra Tree Classifier and Gaussian Process Classifier machine learning methods have been used to predict whether individuals have diabetes or not. In this study, the method with the highest prediction accuracy was determined as the Random Forest Classifier. The accuracy of the recommended method was 81.71%. The proposed method was developed to assist clinicians in predicting diabetic patients using diagnostic measurements. The machine learning methods developed in this study were applied using Colab Notebook a Google Cloud Computing service.