Mahabub (2019) tested different ensemble learning techniques, such as AdaBoost, gradient boost, XGBoost, random forest, etc., to predict diabetes, considering several clinical parameters such as pregnancy, skin thickness, glucose, insulin, blood pressure, diabetes pedigree function, body mass index (BMI), age, and class variable (outcome). They achieved the highest accuracy rate of 84.42% with the multilayer perceptron algorithm. Mushtaq et al. (2022) proposed an optimised model using a voting classification based on the ensemble method to predict diabetes using the Pima diabetes dataset. The gene discussed is INS; the disease is diabetes mellitus.