# Attention-Oriented CNN Method for Type 2 Diabetes Prediction
## 2 Related Work  
We discuss existing work from two perspectives, data preprocessing and model classification, to highlight the research contributions of this article. We focus on studies published in recent years, as the latest research shows improved performance in the diabetes field. For a summary of the historical and current performance of algorithms in diabetes research, see the literature review [15]. In recent years, many methods for diabetes risk prediction studies have been proposed and published. They use various dataset-preprocessing methods to preprocess the data and use various machine learning methods for risk prediction. Commonly used methods include support vector machine (SVM) [16], artificial neural network (ANN) [17], AdaBoost (AB) [18], logistic regression (LR) [19], decision tree (DT) [20] and random forest (RF) [21], etc. In [22], [PERSON] et al. use the group median and median to fill missing values and outliers respectively, and use RF for feature selection. SVM, NB, LR, DT, RF, AB, Gaussian Process Classification (GPC) and Quadratic Discriminant Analysis (QDA) are used for classification, among which RF has the best performance with an accuracy of 92.26%. In [23], [PERSON] et al. use LR for feature selection, and use NB, DT, AB and RF to predict patients with diabetes. RF has the best performance and an accuracy of 94.25%. In [24], [PERSON] and [PERSON] use the ANN, Naive Bayes (NB), DT and DL methods for classification. Among them, the DL method achieves an accuracy of 98.07%. In [25], [PERSON] and [PERSON] use analysis of variance and LR for feature selection, and SVM, LR, DT, RF, ANN and ensembling for classification, among which RF achieves the best performance with an accuracy of 90%. In [26], [PERSON] et al. use outlier rejection, missing value filling, data standardization, and feature selection to preprocess the data, and use KNN, DT, RF, AB, NB, XB, multi-layer perceptron (MLP) and ensembling for classification. Ensembling has the best performance, and the area under the ROC curve (AUC) achieves a score of 95%. In [27], [PERSON] et al. use principal component analysis (PCA) and mean for feature selection and missing value filling before using SVM and RF for classification. They compare the classification performance of RF and SVM, where the RF model is the best, with an accuracy of 83%. In [28], [PERSON] et al., after preprocessing the dataset, use eight ML models including DT, RF and neural network (NN) for classification, among which NN achieves the best performance with an accuracy of 88.6%. In [29], [PERSON] et al. use Spearman correlation for feature selection, polynomial regression for missing value replacement, and their self-designed Quadratic Growth Deep Neural Network (2 GDNN) for classification. The accuracy rates on the PID dataset and LMCH dataset are 97.34% and 97.24%, respectively. In [30], [PERSON] et al. use RF, SVM, LR and NB to test the PIMA Indian dataset, and logistic regression has the highest performance, achieving an accuracy of 82.46%. It fully demonstrates the application and effect of different machine learning models in diabetes prediction and classification. In recent studies, ensemble machine learning models have shown significant potential in diabetes prediction. [PERSON] et al. [31] use a multi-layer feedforward neural network (MLFNN) for classification after preprocessing the PID dataset, achieving an accuracy of 84.17%. Further, [PERSON] et al. [32] propose an innovative integrated hierarchical model by first independently training decision tree (DT) and random forest (RF) models, and then passing the prediction results of these models as input to a neural network, in this way, fusing the output of the previous classifier to achieve higher overall prediction accuracy, with a test accuracy of 83%. Similarly, [PERSON] et al. [33] evaluate the performance of RF, LGBM, LR and SVM after data preprocessing and find that LGBM performs the most outstandingly, with an accuracy rate as high as 86%. [PERSON] et al.