A Lightweight Neural Network Model for Disease Risk Prediction in Edge Intelligent Computing Architecture

Feng Zhou, Shijing Hu (), Xin Du, Xiaoli Wan and Jie Wu
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Feng Zhou: School of Computer Science, Fudan University, Shanghai 200438, China
Shijing Hu: School of Computer Science, Fudan University, Shanghai 200438, China
Xin Du: School of Computer Science, Fudan University, Shanghai 200438, China
Xiaoli Wan: Information Center, Zhejiang International Business Group, Hangzhou 310000, China
Jie Wu: School of Computer Science, Fudan University, Shanghai 200438, China

Future Internet, 2024, vol. 16, issue 3, 1-15

Abstract: In the current field of disease risk prediction research, there are many methods of using servers for centralized computing to train and infer prediction models. However, this centralized computing method increases storage space, the load on network bandwidth, and the computing pressure on the central server. In this article, we design an image preprocessing method and propose a lightweight neural network model called Linge (Lightweight Neural Network Models for the Edge). We propose a distributed intelligent edge computing technology based on the federated learning algorithm for disease risk prediction. The intelligent edge computing method we proposed for disease risk prediction directly performs prediction model training and inference at the edge without increasing storage space. It also reduces the load on network bandwidth and reduces the computing pressure on the server. The lightweight neural network model we designed has only 7.63 MB of parameters and only takes up 155.28 MB of memory. In the experiment with the Linge model compared with the EfficientNetV2 model, the accuracy and precision increased by 2%, the recall rate increased by 1%, the specificity increased by 4%, the F1 score increased by 3%, and the AUC (Area Under the Curve) value increased by 2%.

Keywords: federated learning; edge computing; deep learning; image classification; disease risk prediction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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