A novel solution of deep learning for enhanced support vector machine for predicting the onset of type 2 diabetes

TL;DR

This paper introduces a deep learning-enhanced support vector machine model that improves accuracy, AUC, and processing time for predicting the onset of Type 2 Diabetes, making early diagnosis more practical.

Contribution

It proposes a novel combination of SVM with RBF kernel and LSTM layers to enhance prediction performance and efficiency for Type 2 Diabetes onset.

Findings

Achieved 86.31% accuracy in prediction

Improved AUC to 0.8270

Reduced processing time by 3.8 milliseconds

Abstract

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.