CUDA optimized Neural Network predicts blood glucose control from quantified joint mobility and anthropometrics

TL;DR

This paper presents a CUDA-accelerated neural network that predicts blood glucose control from non-invasive markers, achieving high accuracy and significantly faster training times compared to CPU implementation.

Contribution

The paper introduces a CUDA-optimized neural network for predicting HbA1c levels from non-invasive markers, demonstrating substantial speed improvements and high predictive accuracy.

Findings

Achieved over 95% training accuracy for males and 97% for females.

Reduced training time by 50 times using GPU acceleration.

Identified non-invasive markers correlated with blood glucose control.

Abstract

Neural network training entails heavy computation with obvious bottlenecks. The Compute Unified Device Architecture (CUDA) programming model allows us to accelerate computation by passing the processing workload from the CPU to the graphics processing unit (GPU). In this paper, we leveraged the power of Nvidia GPUs to parallelize all of the computation involved in training, to accelerate a backpropagation feed-forward neural network with one hidden layer using CUDA and C++. This optimized neural network was tasked with predicting the level of glycated hemoglobin (HbA1c) from non-invasive markers. The rate of increase in the prevalence of Diabetes Mellitus has resulted in an urgent need for early detection and accurate diagnosis. However, due to the invasiveness and limitations of conventional tests, alternate means are being considered. Limited Joint Mobility (LJM) has been reported as an indicator for poor glycemic control. LJM of the fingers is quantified and its link to HbA1c is investigated along with other potential non-invasive markers of HbA1c. We collected readings of 33 potential markers from 120 participants at a clinic in south Trinidad. Our neural network achieved 95.65% accuracy on the training and 86.67% accuracy on the testing set for male participants and 97.73% and 66.67% accuracy on the training and testing sets for female participants. Using 960 CUDA cores from a Nvidia GeForce GTX 660, our parallelized neural network was trained 50 times faster on both subsets, than its corresponding CPU implementation on an Intel Core (TM) i7-3630QM 2.40 GHz CPU.