Predictive voting model for early diagnosis of diabetes dataset

This research aimed to construct predictive voting models (hard vote and soft vote) to improve the diabetes diagnosis system at the initial pre-diabetes stage using several risk factors retrieved from a dataset collected from a government hospital in Oman. The study focused on identifying significant predictors of diabetes and enhancing the accuracy of early diagnosis. The Knowledge Discovery in Database (KDD) model was utilized to conduct the experiments. A 33-month historical dataset comprising N = 4104 registered patients and 14 variables was analyzed. The features used for diabetes classification included age, height, weight, gender, diastolic and systolic blood pressure, cholesterol level, blood glucose level, and haemoglobin level. Five supervised classification algorithms were applied to construct the voting models: Decision Tree (J48), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest, and Naïve Bayes. The findings revealed that the hard-vote model achieved the highest predictive accuracy of 84.7% compared with the soft-vote model. Additionally, the haemoglobin A1C test (HbA1c), Fasting Plasma Glucose (FPG), and age were identified as the most significant factors for predicting diabetes. The extracted rules indicated that HbA1c served as the initial criterion for diabetes diagnosis, with a threshold value of 6.3. The study demonstrated the effectiveness of ensemble voting models in improving diabetes prediction during the pre-diabetes stage. The identified predictors and extracted rules may support healthcare professionals in making earlier and more accurate diagnostic decisions. Furthermore, the involvement of domain experts and the validation of rules using classified patient cases strengthened the reliability and practical applicability of the proposed models.