Comparative Analysis of ML Techniques for Predicting Diabetes

Diabetes is a chronic illness that is common around the world, impacting millions of people and creating serious problems for public health. To avoid complications and enhance patient outcomes, diabetes must be identified early and managed well. We report a thorough investigation of the use of ML methods for forecasting diabetes in this publication. We utilize the Diabetes Dataset for Pima Indians (PIDD), a reputable industry benchmark dataset, for our analysis. Our study begins with data preprocessing steps, including addressing missing values, encoding categorical variables, and scaling features. Subsequently, we partition the dataset into sets for testing as well as training to ensure the robustness of our models. We evaluate a range of techniques for ML, such as LR, RF, GB, KNN, SVM, ANN, DT, LSVM, and NB, for their effectiveness in predicting diabetes. A variety of metrics for performance, such as F1-scoring, accuracy, recall, and precision are used to assess every framework once it has been assessed on the testing set after being trained on the training set. Furthermore, we conduct a comparative analysis of the models, considering factors such as interpretability, complexity, and generalization ability. Our results show promising performance across several ML models, with NB and LR exhibiting particularly high accuracy and precision in predicting diabetes. These findings underscore the potential of ML techniques in facilitating early detection and personalized management of diabetes, thereby contributing to improved patient outcomes and healthcare delivery.