Naive Bayes Classifier for Accurate Diabetes Diagnosis and Analysis - Aung, Lynn Htet (INFOTEKS (Information Technology, Computer and Sciences), 2023)
Diabetes mellitus is a chronic metabolic disorder with rising global prevalence, necessitating early and accurate diagnostic tools to mitigate complications. This study investigates the Naive Bayes classifier's efficacy for diabetes diagnosis, leveraging a dataset of 768 patient records encompassing clinical and demographic attributes, such as glucose levels, BMI, and insulin. Data preprocessing steps, including imputation, scaling, and normalization, ensure data quality, while feature selection identifies key predictors to enhance model performance. The classifier achieved an accuracy of 77%, with a weighted F1-score of 0.77, demonstrating robust performance for the