Prediction Model of Ischemic Stroke Recurrence Using PSO-LSTM in Mobile Medical Monitoring System

Abstract

Aiming at the problems of low prediction accuracy and low sensitivity of traditional ischemic stroke recurrence prediction methods, which limits its application range, by introducing an adaptive particle swarm optimization (PSO) algorithm into the Long and Short-Term Memory (LSTM) model, a prediction model of ischemic stroke recurrence using deep learning in mobile medical monitoring system is proposed. First, based on the clustering idea, the particles are divided into local optimal particles and ordinary particles according to the characteristic information and distribution of different particles. By updating the particles with different strategies, the diversity of the population is improved and the problem of local optimal solution is eliminated. Then, by introducing the adaptive PSO algorithm into the LSTM, the PSO-LSTM prediction model is constructed. The optimal super parameters of the model are determined quickly and accurately, and the model is trained combined with the patient's clinical data. Finally, by using SMOTE method to process the original data, the imbalance of positive and negative sample data is eliminated. Under the same conditions, the proposed PSO-LSTM prediction model is compared with two traditional LSTM models. The results show that the prediction accuracy of PSO-LSTM model is 92.0%, which is better than two comparison models. The effective prediction of ischemic stroke recurrence is realized.