A Novel Proposal for Deep Learning-Based Diabetes Prediction: Converting Clinical Data to Image Data

• Published in: Diagnostics (Basel) Vol. 13; no. 4; p. 796
• Main Authors: Aslan, Muhammet Fatih, Sabanci, Kadir
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2023; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Big Data; Classification; convolutional neural network; Data mining; Datasets; Deep learning; Diabetes; diabetes prediction; Disease; Electronic data processing; Feature selection; Health aspects; Insulin resistance; Machine learning; Medical diagnosis; Medical records; Neural networks; numeric-to-image; Pancreas; PIMA dataset; Support vector machines; Technology application; Womens health; Turkey; PIMA dataset; diabetes prediction; support vector machines; numeric-to-image; convolutional neural network
• DOI: 10.3390/diagnostics13040796
• ISSN: 2075-4418

Diabetes, one of the most common diseases worldwide, has become an increasingly global threat to humans in recent years. However, early detection of diabetes greatly inhibits the progression of the disease. This study proposes a new method based on deep learning for the early detection of diabetes. Like many other medical data, the PIMA dataset used in the study contains only numerical values. In this sense, the application of popular convolutional neural network (CNN) models to such data are limited. This study converts numerical data into images based on the feature importance to use the robust representation of CNN models in early diabetes diagnosis. Three different classification strategies are then applied to the resulting diabetes image data. In the first, diabetes images are fed into the ResNet18 and ResNet50 CNN models. In the second, deep features of the ResNet models are fused and classified with support vector machines (SVM). In the last approach, the selected fusion features are classified by SVM. The results demonstrate the robustness of diabetes images in the early diagnosis of diabetes.