Machine Learning–Enhanced Diabetes Identification System

One of the most deadly chronic conditions that raise blood sugar is diabetes. Diabetes can cause a wide range of issues if it is not treated or recognized in time. A patient ultimately visits a diagnostic facility and sees a doctor there rather than going through the time-consuming identification procedure. On the other side, machine learning deals with this important problem. We have created a model that can successfully predict whether a patient will eventually acquire diabetes. Four machine learning classification techniques were utilized in this experiment to identify diabetes early: SVM, Gradient Boosting Classifier, KNN, and Naive Bayes. The studies make use of the PIDD (Pima Indians Diabetes Database) from UCI’s machine learning repository. These measures are used to assess all algorithms’ accuracy, precision, F-measure, and recall. Accuracy is assessed using instances that were properly and erroneously categorized. The findings show that accuracy is better for Support Vector Machines than for other methods. Receiver operating characteristic (ROC) curves are used to correctly and methodically confirm these findings. We are able to decrease the number of features that must be included or deleted in our prediction model, improving its accuracy, by combining Recursive Feature Elimination with hyperparameter adjustment.