# Attention-Oriented CNN Method for Type 2 Diabetes Prediction
## 6 Conclusions  
The prediction of diabetes is a very challenging task. In this paper, we propose a stable T2 DM prediction framework aimed at improving the performance of diabetes prediction on the NHANES dataset and PID dataset. The framework employs a series of preprocessing steps, including missing value filling, outlier replacement, feature importance analysis, data standardization, and sample class weight assignment. And we propose the SECNN model and CNN model. Furthermore, our framework is also applicable to traditional ML models, which all show good performance in handling T2 DM classification problems. On the PID dataset, the SECNN model achieves an accuracy of 89.4%, and the CNN model achieves an accuracy of 87.58%. On the NHANES dataset, the SECNN model achieves an accuracy of 94.12%, and the CNN model achieves an accuracy of 93.18%. Our proposed model exhibits excellent performance in T2 DM prediction. These results demonstrate that our proposed SECNN model has excellent performance in T2 DM prediction. Whether on the PID dataset or the NHANES dataset, the SECNN model can more accurately predict T2 DM patients. This further verifies the superiority and adaptability of our proposed model in T2 DM prediction.  
Our research results show that the SECNN model exhibits excellent performance in predicting T2 DM, validating the view that introducing the SE module can significantly improve performance. Furthermore, our proposed deep learning model shows better performance compared to traditional machine learning (ML) models. Therefore, we conclude that the T2 DM prediction framework developed in this study incorporates advanced preprocessing steps and multiple machine learning models to significantly improve the prediction accuracy of type 2 diabetes. Experiments on NHANES and PID datasets verify the effectiveness of this framework. In particular, the SECNN model has proven its important value in the field of diabetes prediction with its excellent stability and excellent prediction performance. Furthermore, our method not only performs diabetes prediction but also provides insights into understanding the pathogenesis of diabetes. Therefore, this study not only provides an effective tool for the early diagnosis and treatment of T2 DM but also deepens our understanding of the progression of the disease.  
Conceptualization, H.G.; methodology, H.G.; software, H.G.; validation, H.G., T.A. and C.Y.; formal analysis, H.G.; investigation, H.G.; resources, J.Z.; data curation, L.S.; writing--original draft preparation, H.G.; writing--review and editing, J.Z. and Z.K.; visualization, H.G.; supervision, J.Z. and Z.K.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Jilin Provincial Department of Science and Technology (Nos. YDZJ202303 CGZH010, YDZJ202301 ZYTS496, 20230401092 YY, 20210101477 JC).  
Not applicable.  
Data Availability Statement: The data presented in this study are available in publicly available databases. The PIMA Indian Diabetes Dataset is accessible through the Kaggle platform at: [[https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database](https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database)]([https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database](https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database)) (accessed on 3 May 2024); Dataset from the National Health and Nutrition Examination Surveys (NHANES) are available through the Centers for Disease Control and Prevention's NHANES database website at [[https://www.cdc.gov/nchs/nhanes/index.htm](https://www.cdc.gov/nchs/nhanes/index.htm)]([https://www.cdc.gov/nchs/nhanes/index.htm](https://www.cdc.gov/nchs/nhanes/index.htm)) (accessed on 3 May 2024).  
The authors declare no conflicts of interest.