Diabetes Detection and Analysis Using Machine Learning

One of the most lethal chronic illnesses is diabetes, which leads to high blood sugar levels. Many complications might arise from diabetes if it is not properly diagnosed or treated. Seeing a doctor and going through the identification procedure takes too much time, therefore patients usually resort to diagnostic centers instead. However, this major problem can be solved with machine learning. In order to correctly forecast whether or not a patient would acquire diabetes, we have created a model. In this study, four different machine learning classification methods were utilized to diagnose diabetes at an early stage: SVM, Gradient Boosting Classifier, KNN, and Naive Bayes. UCI’s machine learning repository provides the PIDD (Pima Indians Diabetes Database) for use in the tests. These measures are used to assess the algorithms' accuracy, precision, F-measure, and recall. Accuracy is measured by comparing instances with the right and wrong labels. The findings show that Support Vector Machine is the most effective algorithm. These results are verified in a thorough and methodical manner by using ROC curves. Reduce the number of features that must be included or deleted in our prediction model by combining Recursive Feature Elimination with hyperparameter adjustment.