Attention-Oriented CNN Method for Type 2 Diabetes Prediction

• Published in: Applied sciences Vol. 14; no. 10; p. 3989
• Main Authors: Zhao, Jian, Gao, Hanlin, Yang, Chen, An, Tianbo, Kuang, Zhejun, Shi, Lijuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2024
• Subjects: Accuracy; Analysis; Antibodies; Classification; convolutional neural network; data preprocessing; Datasets; Decision trees; deep learning; Diabetes; Diabetes therapy; Feature selection; Glucose; Hyperglycemia; Insulin; Machine learning; Medical research; Methods; Neural networks; Patients; Performance evaluation; Regression analysis; Support vector machines; Type 2 diabetes; Variance analysis; Weight control; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14103989

Diabetes is caused by insulin deficiency or impaired biological action, and long-term hyperglycemia leads to a variety of tissue damage and dysfunction. Therefore, the early prediction of diabetes and timely intervention and treatment are crucial. This paper proposes a robust framework for the prediction and diagnosis of type 2 diabetes (T2DM) to aid in diabetes applications in clinical diagnosis. The data-preprocessing stage includes steps such as outlier removal, missing value filling, data standardization, and assigning class weights to ensure the quality and consistency of the data, thereby improving the performance and stability of the model. This experiment used the National Health and Nutrition Examination Survey (NHANES) dataset and the publicly available PIMA Indian dataset (PID). For T2DM classification, we designed a convolutional neural network (CNN) and proposed a novel attention-oriented convolutional neural network (SECNN) through the channel attention mechanism. To optimize the hyperparameters of the model, we used grid search and K-fold cross-validation methods. In addition, we also comparatively analyzed various machine learning (ML) models such as support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), and artificial neural network (ANN). Finally, we evaluated the performance of the model using performance evaluation metrics such as precision, recall, F1-Score, accuracy, and AUC. Experimental results show that the SECNN model has an accuracy of 94.12% on the NHANES dataset and an accuracy of 89.47% on the PIMA Indian dataset. SECNN models and CNN models show significant improvements in diabetes prediction performance compared to traditional ML models. The comparative analysis of the SECNN model and the CNN model has significantly improved performance, further verifying the advantages of introducing the channel attention mechanism. The robust diabetes prediction framework proposed in this article establishes an effective foundation for diabetes diagnosis and prediction, and has a positive impact on the development of health management and medical industries.