An Empirical Model to Predict the Diabetic Positive Using Stacked Ensemble Approach - R., Sivashankari; M., Sudha; Hasan, Mohammad Kamrul; Saeed, Rashid A.; Alsuhibany, Suliman A.; Abdel-Khalek, Sayed (Frontiers in Public Health, 2022)
Nowadays, disease detection automation is widespread in healthcare systems. The diabetic disease is a significant problem, which has spread widely all over the world. It is one of the genetic diseases that cause human life from troublesome to death. Every year the number of people with diabetes rises by millions and affects children too. The disease identification involves manual so far, and automation is a current trend in the medical field. Existing methods are used a single algorithm for the prediction of diabetes. For complex problems, a single model is not enough because it may not be suitable for the input data or the parameters used in the approach, and so on. So, to solve complex problems, multiple algorithms are used nowadays. These multiple algorithms follow a homogeneous model or heterogeneous model. The homogeneous model means the same algorithm, but the model has been used multiple times. In the heterogeneous model, different algorithms are used. This paper adopts a heterogeneous ensemble model called the stacked ensemble model to predict whether a person has diabetes positively or negatively. This stacked ensemble model is advantageous in the prediction. Compared to other existing models such as logistic regression Naïve Bayes (72%), (74.4%), and LDA (81%), the proposed stacked ensemble model has achieved 93% accuracy in predicting blood sugar disease.