# Early Risk Prediction of Diabetes Based on GA-Stacking
###### Abstract
Scholars began to try different feature selection methods. [PERSON] et al. [13] used three diabetes datasets to evaluate the prediction ability of the model combined with nine feature selection algorithms and 35 machine learning algorithms. They found that performing feature selection operations on the dataset before the classification model can reduce the execution time while avoiding data overfitting. Feature selection methods are mainly divided into filtering method, packaging method, and embedding method. GA based on the input wrapping method can successfully extract attribute subsets and improve the accuracy of prediction. [PERSON] et al. [14] used GA to reduce the attribute set. GA was combined with the random forest to predict multiple types of fault diagnosis for spur gears. When the original condition attribute set is reduced by 34%, 97% classification accuracy can still be obtained. In the field of diabetes risk prediction, [PERSON] et al. [15] combined GA with K-means clustering algorithm for feature selection and then used KNN for classification. Compared with previous related studies, the model performed better. As an important means of early screening of risk factors of diabetes and other chronic diseases, physical examination data provide the possibility for machine learning to assist doctors in early diabetes risk prediction [16]. However, due to the large amount of attribute sets of physical examination data, the performance of the classification model is limited. Moreover, the models used for diabetes risk prediction generally have weak generalization ability and are only suitable for a single dataset. Therefore, GA-stacking ensemble learning model is proposed in this paper. Firstly, GA based on DT is used to extract attribute subsets of preprocessed data. Secondly, the training dataset is subjected to five-fold cross-validation, where the four-fold data are used to train CNN and SVM as the primary learners of stacking, and the remaining one-fold data are classified by the primary learner as the training dataset of the fully connected layer to avoid the risk of over-fitting. The main highlights of this article are as follows:GA-stacking ensemble learning model is proposed in this paper. According to stacking, the proposed model is combined with the attribute extraction ability of CNN and the advantages of SVM to deal with binary classification problems. In addition, through the neurons in a fully connected layer, the classification error rate of the primary learner is decreased. After preprocessing the physical examination dataset and early-stage diabetes risk prediction dataset, we implemented KNN, SVM, LR, NB, CNN, and XBNet proposed in [17]. The proposed GA-stacking not only outperforms the prediction performance and efficiency of the basic machine learning algorithm and the model implemented in the latest studies, but it also has stronger generalization capabilities. The rest of the paper is structured as follows. Materials and methods used are illustrated in Section 2. GA-stacking is proposed and presented in Section 3. Experiments and results analysis are presented in Section 4. The last section summarizes the main work and future research directions of this paper.