Around the world, type 1 diabetes (T1D) is being found in more among children. People with type 1 diabetes who were diagnosed at a young age are more likely to have heart disease and other problems linked to their diabetes. The Diabetes Control and Complications Trial (DCCT) showed that strict glycemic management lowers the chance of diabetes complications in people with type 1 diabetes. Teenagers with T1D had a higher amount of glycated hemoglobin A1c (HbA1c) than adults, even though they needed more insulin every day and gained weight. This suggests that insulin was less effective at keeping their blood sugar under control [4]. A rise in hormones related to puberty, like growth hormone and sex steroids, may be linked to insulin resistance in teens. So, the best way to treat T1D in teens is to focus on treatments that make the body more sensitive to insulin. This work finds that the When compared to other methods, both the Naive Bayes Network Updateable and the Naive Bayes itself produce the best results with an accuracy of 76.30%. When compared to the other stumps, the Decision one has the lowest accuracy, at only 71.88%. When compared to other methods, both the Naive Bayes Network Updateable and the Naive Bayes itself produce the same, best results with a precision of 0.76. When compared to the other stumps, the Decision stump has the lowest accuracy, at 0.72.the Naive Bayes Network Updateable and Naive Bayes both achieve the greatest results and have the same recall of 0.76 compared to other methods. When compared to the other stumps, the Decision one has the lowest recall (0.72).Naive Bayes and the Naive Bayes Network Updateable both produce the best results when compared to other methods, with a kappa of 0.47. When compared to the other stumps, the Decision one has the lowest kappa value (0.37).When compared to other methods, Naive Bayes and Naive Bayes Network Updateable get the best results (0.76 F-Measure). The lowest value is 0.72, which is held by the Decision Stump. When compared to other methods, Naive Bayes produces the best results (0.49 MCC). When compared to the other stumps, the Decision one yields the lowest MCC value (0.38 MCC). The MCC for the Naive Bayes Updateable is 0.47. The Naive Bayes, Naive Bayes Network Updateable, and Random Forest all achieve the same best-in-class result of 0.82 ROC. The Decision Stump has the lowest ROC of the available options, at 0.68. The greatest results, 0.82 PRC, are shared by the Naive Bayes, Naive Bayes Network Updateable, and Random Forest methods. When compared to other PRC values, the Decision Stump's 0.68 PRC is the lowest. The statistical learning approach shows least deviations compare with other models.