TY - JOUR
T1 - Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization
AU - Alqushaibi, Alawi
AU - Hasan, Mohd Hilmi
AU - Abdulkadir, Said Jadid
AU - Muneer, Amgad
AU - Gamal, Mohammed
AU - Al-Tashi, Qasem
AU - Taib, Shakirah Mohd
AU - Alhussian, Hitham
N1 - Publisher Copyright:
© 2023 Tech Science Press. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model’s poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.
AB - Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model’s poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.
KW - Bayesian optimization
KW - convolutional neural network
KW - diabetes mellitus
KW - SMOTE
KW - Type 2 diabetes
UR - http://www.scopus.com/inward/record.url?scp=85154609438&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85154609438&partnerID=8YFLogxK
U2 - 10.32604/cmc.2023.035655
DO - 10.32604/cmc.2023.035655
M3 - Article
AN - SCOPUS:85154609438
SN - 1546-2218
VL - 75
SP - 3223
EP - 3238
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 2
ER -