Year: 2024
Type: conference-paper
Abstract: In order to solve the problem of accuracy and timeliness of stroke prediction on wearable devices, we proposed an improved model. This model combines advantages of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Kolmogorov-Arnold Network (KAN). We use CNN to extract features, process time series through LSTM and predict by KAN, thereby constructing an efficient stroke prediction model. Experiment results shows the accuracy of 0.92, demonstrating the model’s excellent performance in stroke prediction tasks on wearable devices.
Authors: Yao Lu, Thinagaran Perumal, Chengzhi Liu
Institution: Universiti Putra Malaysia
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4411747399