Naive Bayes Classifier for Accurate Diabetes Diagnosis and Analysis

Abstract

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 "Not Worthy" class but moderate results for the "Worthy" class due to class imbalance and overlapping features. Ensemble methods, such as bagging and boosting, were explored to address these challenges, further improving robustness and recall. The study highlights the Naive Bayes classifier as a cost-effective, computationally efficient tool for real-time diabetes detection, with potential for deployment in resource-limited healthcare settings. Future research should focus on class balancing, advanced feature engineering, and validation on larger, diverse datasets to enhance diagnostic reliability and scalability.