Al Sadi, K.; Balachandran, W. Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman. Bioengineering 2023, 10, 1420, doi:10.3390/bioengineering10121420.
Al Sadi, K.; Balachandran, W. Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman. Bioengineering 2023, 10, 1420, doi:10.3390/bioengineering10121420.
Al Sadi, K.; Balachandran, W. Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman. Bioengineering 2023, 10, 1420, doi:10.3390/bioengineering10121420.
Al Sadi, K.; Balachandran, W. Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman. Bioengineering 2023, 10, 1420, doi:10.3390/bioengineering10121420.
Abstract
Abstract: The surge of diabetes poses a significant global health challenge, particularly in Oman and the Middle East. Early detection of diabetes is crucial for proactive intervention and improved patient outcomes. This research leverages the power of machine learning, specifically, Convolutional Neural Networks (CNNs), to develop an innovative 4D CNN model dedicated to early diabetes prediction. A region-specific dataset from Oman is utilized to enhance health outcomes for individuals at risk of developing diabetes. The proposed model showcases remarkable accuracy, achieving an average accuracy of 98.49% to 99.17% across various epochs. Additionally, it demonstrates excellent F1 score, recall, and sensitivity, highlighting its ability to identify true positive cases. The findings contribute to the ongoing effort to combat diabetes and pave the way for future research in using deep learning for early disease detection and proactive healthcare.
Keywords
Deep learning; Convolutional Neural Networks (CNN); K-Nearest Neighbors (KNN), Diabetes type II
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
