TY - JOUR
T1 - A novel solution of deep learning for enhanced support vector machine for predicting the onset of type 2 diabetes
AU - Shrestha, Marmik
AU - Alsadoon, Omar Hisham
AU - Alsadoon, Abeer
AU - Al-Dala’in, Thair
AU - Rashid, Tarik A.
AU - Prasad, P. W. C.
AU - Alrubaie, Ahmad
PY - 2023/2
Y1 - 2023/2
N2 - Type 2 Diabetes is one of the most major and fatal diseases known to human beings, where thousands of people are subjected to the onset of Type 2 Diabetes every year. However, the diagnosis and prevention of Type 2 Diabetes are relatively costly in today’s scenario; hence, the use of machine learning and deep learning techniques is gaining momentum for predicting the onset of Type 2 Diabetes. This research aims to increase the accuracy and Area Under the Curve (AUC) metric while improving the processing time for predicting the onset of Type 2 Diabetes. The proposed system consists of a deep learning technique that uses the Support Vector Machine (SVM) algorithm along with the Radial Base Function (RBF) along with the Long Short-term Memory Layer (LSTM) for prediction of onset of Type 2 Diabetes. The proposed solution provides an average accuracy of 86.31% and an average AUC value of 0.8270 or 82.70%, with an improvement of 3.8 milliseconds in the processing. Radial Base Function (RBF) kernel and the LSTM layer enhance the prediction accuracy and AUC metric from the current industry standard, making it more feasible for practical use without compromising the processing time.
AB - Type 2 Diabetes is one of the most major and fatal diseases known to human beings, where thousands of people are subjected to the onset of Type 2 Diabetes every year. However, the diagnosis and prevention of Type 2 Diabetes are relatively costly in today’s scenario; hence, the use of machine learning and deep learning techniques is gaining momentum for predicting the onset of Type 2 Diabetes. This research aims to increase the accuracy and Area Under the Curve (AUC) metric while improving the processing time for predicting the onset of Type 2 Diabetes. The proposed system consists of a deep learning technique that uses the Support Vector Machine (SVM) algorithm along with the Radial Base Function (RBF) along with the Long Short-term Memory Layer (LSTM) for prediction of onset of Type 2 Diabetes. The proposed solution provides an average accuracy of 86.31% and an average AUC value of 0.8270 or 82.70%, with an improvement of 3.8 milliseconds in the processing. Radial Base Function (RBF) kernel and the LSTM layer enhance the prediction accuracy and AUC metric from the current industry standard, making it more feasible for practical use without compromising the processing time.
UR - https://hdl.handle.net/1959.7/uws:70014
U2 - 10.1007/s11042-022-13582-9
DO - 10.1007/s11042-022-13582-9
M3 - Article
SN - 1380-7501
VL - 82
SP - 6221
EP - 6241
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 4
ER -