# Early Risk Prediction of Diabetes Based on GA-Stacking
###### Abstract  
Early risk prediction of diabetes could help doctors and patients to pay attention to the disease and intervene as soon as possible, which can effectively reduce the risk of complications. In this paper, a GA-stacking ensemble learning model is proposed to improve the accuracy of diabetes risk prediction. Firstly, genetic algorithms (GA) based on Decision Tree (DT) is used to select individuals with high adaptability, that is, a subset of attributes suitable for diabetes risk prediction. Secondly, the optimized convolutional neural network (CNN) and support vector machine (SVM) are used as the primary learners of stacking to learn attribute subsets, respectively. Then, the output of CNN and SVM is used as the input of the mate learner, the fully connected layer, for classification. Qingdao desensitization physical examination data from 1 January 2017 to 31 December 2019 is used, which includes body temperature, BMI, waist circumference, and other indicators that may be related to early diabetes. We compared the performance of GA-stacking with K-nearest neighbor (KNN), SVM, logistic regression (LR), Naive Bayes (NB), and CNN before and after adding GA through the average prediction time, accuracy, precision, sensitivity, specificity, and F1-score. Results show that prediction efficiency can be improved by adding GA. GA-stacking has higher prediction accuracy. Moreover, the strong generalization ability and high prediction efficiency of GA-stacking have also been verified on the early-stage diabetes risk prediction dataset published by UCI. diabetes; machine learning; stacking; CNN; SVM; GA +
Footnote †: journal: Medical  
shifted from traditional machine learning algorithms to deep learning algorithms such as neural network (NN), CNN, and long short-term memory (LSTM) with higher accuracy. [PERSON] and [PERSON] [5] applied NN to diabetes risk prediction on early-stage diabetes risk prediction dataset. Its AUC and accuracy rates are 98.3% and 98.1%, respectively, which are better than machine learning algorithms such as NB, KNN, SVM, and RF. They proved that NN can be used for diabetes prediction. [PERSON] et al. [6] used the Conv-LSTM-based model for diabetes risk prediction for the first time. They compared Conv-LSTM, T-LSTM, CNN, and CNN-LSTM using the Pima Indian Diabetes dataset. Among them, the Conv-LSTM was superior to the other three models in terms of classification accuracy, which showed that the attribute extraction ability of CNN can improve the accuracy of the classification model. Generally, the performance of one classification model is limited. Besides, there are some problems of it such as weak generalization ability and poor fault tolerance, which makes the effect of disease prediction and diagnosis not ideal. Therefore, ensemble learning strategies have emerged [7]. Ensemble learning built a model based on the idea of integrating weak classifiers into strong classifiers. [PERSON] [8] proposed the stacking ensemble strategy that can minimize the error rate of one or more generalizations. Compared with the boosting method proposed by [PERSON] et al. and the bagging method proposed by [PERSON], the original training data set is used by stacking to train the primary learner. Then, the output of the primary learner is processed by the meta learner, which improved the ability of one model [9]. Stacking is now used in multiple fields. [PERSON] et al. [10] used stacking ensemble strategy for breast cancer-related amino acid sequence prediction. NB, KNN, SVM, and RF are used as primary learners of the proposed model. Genetic programming (GP) is used as the meta learner. Compared to basic machine learning algorithms and other traditional ensemble strategies, the model proposed by [PERSON] had more steady performance. The key to using machine learning algorithms to assist diabetes risk prediction is to extract valid information from the attributes set. However, the large dimension of the attributes set has brought a huge impact on training [11; 12].