Diagnosis of Diabetes Using Data Mining Techniques

Abstract

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels due to the body's inability to produce or effectively use insulin. It is a significant and growing health concern worldwide, affecting millions of individuals and posing a considerable burden on healthcare systems. Diabetes can lead to various complications, including cardiovascular disease, kidney damage, nerve damage, and vision impairment. Early and accurate diagnosis of diabetes is crucial for effective management and prevention of complications. Traditional diagnostic methods rely on clinical symptoms, medical history, and laboratory tests. However, with the advancements in technology and the availability of large healthcare datasets, data mining techniques have emerged as a promising approach for diabetes diagnosis. Diabetes diagnosis plays a crucial role in effective disease management and prevention of complications. This study explores the application of data mining techniques for diabetes diagnosis, aiming to improve diagnostic accuracy and support healthcare decision-making. Four classification models (Decision Tree, SVM, Naïve Bayes, and Neural Network) are constructed and evaluated using a comprehensive diabetes dataset. Performance evaluation metrics, including TP rate, FP rate, accuracy, precision, recall, and F1-score, are employed to assess the models. The results indicate that the Decision Tree model consistently outperformed the other models, achieving high accuracy and F1-scores in both evaluation scenarios. The Naïve Bayes, SVM, and Neural Network models also showed reasonable performance, although slightly lower than the Decision Tree model. Based on the findings, it is recommended to utilize the Decision Tree model for diabetes diagnosis due to its high accuracy and F1-scores. However, further research and validation on larger and more diverse datasets are needed to ensure the generalizability of these results. Additionally, exploring the combination of different data mining techniques or the incorporation of additional factors, such as genetic data or lifestyle factors, could enhance the predictive capabilities of the models.