<![CDATA[Abstract - Data mining is crucial for extracting patterns and valuable insights from extensive datasets, utilizing artificial intelligence and advanced data analysis techniques across various domains. Diabetes, a metabolic disorder characterized by elevated blood glucose levels, poses significant health risks, including cardiovascular and renal complications if untreated. Data mining plays a pivotal role in exploring and predicting diabetes by identifying high-risk populations, thereby enabling early intervention strategies such as lifestyle modifications and timely treatment initiation. Analyzing comprehensive datasets encompassing diabetes-related factors such as weight, blood pressure, blood glucose levels, and genetic predispositions data mining constructs predictive models to assess risks and implement targeted interventions. In a comprehensive study involving 768 cases (268 positive and 500 negative) Logistic Regression achieved 70% accuracy, with a recall of 57% and an F1 score of 0.63 , Naive Bayes (GaussianNB) achieved 68% accuracy, with a recall rate of 54% and an F1 score of 0.61, Decision Tree Classifier achieved 66% accuracy, with a recall rate of 62% and an F1 score of 0.64 , Random Forest achieved 70% accuracy, with a recall rate of 59% and an F1 score of 0.64 , XGBClassifier achieved 66% accuracy, with a recall rate of 58% and an F1 score of 0.62. The analysis underscores a trade-off between precision and recall, particularly in classifying high-risk diabetes cases. High precision reduces false positives but may lower recall, potentially missing true positive cases. Conversely, emphasizing recall may increase false positives. Achieving a balance between these metrics is critical for effective diabetes prediction and tailored healthcare strategies This abstract encapsulates the pivotal role of data mining in diabetes research, emphasizing its impact on predictive modeling and healthcare decision making.]]>
