  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.
