Prediction of Diabetes using Machine Learning Algorithms and Correlation with Pregnancy

When the body is unable to control blood glucose levels, diabetes, a chronic medical condition, develops. Pregnant women who have gestational diabetes mellitus (GDM) are affected by this form of diabetes. The Polyisocyanurate Insulation Manufacturers Association (PIMA) data set was used to demonstrate how different machine learning (ML) algorithms could predict GDM. Metrics are used to verify how accurate various ML algorithms are. Using a confusion matrix, ML algorithms’ importance is illustrated. Data on 768 patients and the nine distinguishing characteristics that correspond to them are included in the collection. The dataset was used to predict diabetes using seven ML algorithms. We discovered that Naive Bayes, Support Vector Machine (SVM) and Logistic Regression integrated model does a good job of predicting diabetes.