Abstract
Today, disease detection automation is widespread in healthcare systems. The diabetic disease is a significant problem that has spread widely all over the world. It is a genetic disease that causes trouble for human life throughout the lifespan. Every year the number of people with diabetes rises by millions, and this affects children too. The disease identification involves manual checking so far, and automation is a current trend in the medical field. Existing methods use 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. To solve complex problems, multiple algorithms are used. 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 93.1% accuracy in predicting blood sugar disease.
Highlights
IntroductionThree primary reasons that a person may suffer from diabetes are genetics, lifestyle, and environment
People’s regular foods contain a vast amount of carbohydrates and calories
The proposed model has outperformed in terms of prediction of diabetic positive compared to other existing models and has achieved 93% accuracy as a detection rate
Summary
Three primary reasons that a person may suffer from diabetes are genetics, lifestyle, and environment. The first reason for diabetic positivity is genetics. Studies proved that the children whose parents are type 2 [Muoio and Newgard [1]] diabetic have three times more chances to develop diabetic positive than the parents who don’t have diabetic positive. Lifestyle is the second reason for the diabetic positive because proven studies show that the individual lifestyle causes diabetic positive even though their ancestors are not diabetic positive. The third reason for the diabetic positive is adopting intricate weight loss mechanisms. It causes kidney failure or heart issues that lead to diabetes positive in the future
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