DiabDeep: Pervasive Diabetes Diagnosis Based on Wearable Medical Sensors and Efficient Neural Networks

Abstract

Diabetes impacts the quality of life of millions of people around the globe. However, diabetes diagnosis is still an arduous process, given that this disease develops and gets treated outside the clinic. The emergence of wearable medical sensors (WMSs) and machine learning points to a potential way forward to address this challenge. WMSs enable a continuous, yet user-transparent, mechanism to collect and analyze physiological signals. However, disease diagnosis based on WMS data and its effective deployment on resource-constrained edge devices remain challenging due to inefficient feature extraction and vast computation cost. To address these problems, we propose a framework called DiabDeep that combines efficient neural networks (called DiabNNs) with off-the-shelf WMSs for pervasive diabetes diagnosis. DiabDeep bypasses the feature extraction stage and acts directly on WMS data. It enables both an (i) accurate inference on the server, e.g., a desktop, and (ii) efficient inference on an edge device, e.g., a smartphone, to obtain a balance between accuracy and efficiency based on varying resource budgets and design goals. On the resource-rich server, we stack sparsely connected layers to deliver high accuracy. On the resource-scarce edge device, we use a hidden-layer long short-term memory based recurrent layer to substantially cut down on computation and storage costs while incurring only a minor accuracy loss. At the core of our system lies a grow-and-prune training flow: it leverages gradient-based growth and magnitude-based pruning algorithms to enable DiabNNs to learn both weights and connections, while improving accuracy and efficiency. We demonstrate the effectiveness of DiabDeep through a detailed analysis of data collected from 52 participants. For server (edge) side inference, we achieve a 96.3 percent (95.3 percent) accuracy in classifying diabetics against healthy individuals, and a 95.7 percent (94.6 percent) accuracy in distinguishing among type-1 diabetic, type-2 diabetic, and healthy individuals. Against conventional baselines, such as support vector machines with linear and radial basis function kernels, k-nearest neighbor, random forest, and linear ridge classifiers, DiabNNs achieve higher accuracy, while reducing the model size (floating-point operations) by up to 454.5× (8.9×). Therefore, the system can be viewed as pervasive and efficient, yet very accurate.