# Attention-Oriented CNN Method for Type 2 Diabetes Prediction
## 3 Materials and Methods
### Datasets  
Dataset Description  
This study utilized the National Health and Nutrition Examination Survey (NHANES) and publicly available PIMA India (PID) datasets. The NHANES dataset includes demographic, questionnaire, physical examination, and laboratory data from 1999-2000 to 2015-2016 for a total of 37,079 cases, including 4852 diabetic and 32,227 non-diabetic patients. This dataset is not only used in diabetes research but also widely used in the research of other diseases, such as coronary heart disease [36]. As part of an effort to assess the \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Authors** & **Year** & **Classification** & **Comments** \\ \hline [PERSON] et al. [22] & 2018 & SVM, NB, LDA, LR, DT, RF, AB, GPC, QDA & RF has the best performance, with an accuracy of 92.26\%. \\ [PERSON] et al. [23] & 2020 & NB, DT, AB, RF & The RF model is the best, with an accuracy of 94.25\%. \\ [PERSON] et al. [24] & 2020 & ANN, NB, DT, DL & The DL method has the best performance, with an accuracy of 98.07\% \\ [PERSON] et al. [25] & 2020 & SVM, LR, DT, RF, ANN, Ensembling & RF performance is the best, with an accuracy rate of 90\%. \\ [PERSON] et al. [7] & 2020 & CNN & The accuracy rate reached 89.0\%. \\ [PERSON] et al. [26] & 2020 & KNN, DT, AB, RF, NB, XB, Ensembling & Ensembling has the best performance, with an AUC value of 95\% \\ [PERSON] et al. [31] & 2020 & MLFNN & The accuracy of MLFNN is 84.17\% \\ [PERSON] et al. [32] & 2020 & Ensemble model (ANN + LR + DT) & The accuracy of Ensemble model is 83\% \\ [PERSON] et al. [27] & 2021 & SVM, RF & The RF model is the best, with an accuracy of 83\%. \\ [PERSON] et al. [33] & 2021 & RF, LGBM, LR, SVM & LGBM has the best performance, with an accuracy of 86\% \\ [PERSON] et al. [28] & 2021 & DT, KNN, RF, NB, AB, LR, SVM, NN & NN has the best performance, with an accuracy of 88.6\%. \\ [PERSON] et al. [29] & 2022 & RF, SVM, 2 GDNN & The accuracy rates of the 2 GDNN model are 97.34\% and 97.24\% respectively. \\ [PERSON] et al. [34] & 2022 & SVM, ANN, FMDP & The prediction accuracy of FMDP is 94.87\% \\ [PERSON] et al. [35] & 2023 & EMLM & The accuracy rate was 97\%. \\ [PERSON] et al. [30] & 2023 & RF, SVM, LR, NB & LR has the best performance, with an accuracy of 82.46\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature summary. health and nutritional status of adults and children in the United States, this dataset collects a wide range of data from basic demographic information to detailed biochemical markers. The 51 variables covered include basic demographic characteristics such as gender, age, annual household income, and the ratio of household income to the poverty line. Additionally, the dataset includes detailed physiological measurements, such as body mass index, blood pressure, weight, and height. In terms of hematological indicators, multiple parameters, such as white blood cells, red blood cells, and hemoglobin, were analyzed. In terms of biochemical markers, key parameters such as cholesterol, creatinine and glucose are collected, which are extremely important for assessing an individual's health status. Lifestyle factors such as physical labor intensity and health insurance status are also taken into consideration, as well as family history of chronic diseases such as diabetes and heart disease. People with diabetes are marked as 0 in the dataset, and people without diabetes are marked as 1. In this study, the feature importance is analyzed, and a description of the 25 key variables that are screened out is shown in Table 2.  
The latter is composed of information from 768 women near Phoenix, Arizona, USA [37]. There are cases in pregnant women and can therefore be considered to include data on gestational diabetes. In this dataset, there are 268 instances of diabetics and 500 instances of non-diabetics. The dataset contains 9 different attributes, consisting of 8 independent variables and 1 dependent variable, and outcome is used as the diabetes target variable.