A Case Study Of Machine Learning Methodologies For The Diabetic Prediction

Abstract

Early accurate prediction technologies are essential for managing long-term diabetes because the disease remains a significant global health issue. Metabolic disorder known as diabetes causes high blood sugar in individuals because the body fails to produce or use insulin effectively. Uncontrolled diabetes results in health complications that affect the heart as well as cause damage to kidneys and nerves. This research inquiry focuses on different Machine Learning (ML) methods used to predict diabetes risks as well as the identification of its symptoms. Multiple classification technologies such as Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Logistic Regression (LR) and k-Nearest Neighbors are assessed which successfully detect diabetic patterns. The study analyzes data pre-processing techniques which include feature normalization as well as outlier treatment and class sampling because these influence model accuracy levels. The survey highlights the evaluation methods through cross-validation and utilizes accuracy, precision, recall, F1-score and ROC curves to maintain high prediction reliability and robust prediction outcomes. The assessment conducts the thorough analysis of critical difficulties related to data privacy together with model interpretability problems and techniques for managing imbalanced datasets. This survey investigates the combination of medical records with sensor-based data through secure data-sharing techniques for better prediction system performance. Researchers along with healthcare practitioners have the required comprehensive base to understand ML solutions for diabetes prediction through the survey data.