Empathy of Diabetics through Supervised Machine Learning Models

Present-era diabetes is the most dangerous problem for the majority of people. It will be affected irrespective of age fact. In this case, a person's lifestyle can estimate the next stage of affected by comparing it with diabetic patients' reports for future prediction on his diabetic affected stage is one of the approaches. This automation can be done using machine learning approaches with good prediction methodologies. We proposed a study to compare all classification models in machine learning for diabetes prediction using lifestyle data. Based on lifestyle-related and demographic characteristics variables predicting diabetes, all classification models in supervised machine learning. Models are evaluated performance metrics for all models to produce a result as accuracy, sensitivity, specificity, precision, F1 score, and ROC curve with time complexity. These machine-learning models assist medical institutions in identifying diabetes patients