{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T04:29:24Z","timestamp":1775104164029,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T00:00:00Z","timestamp":1599091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The research on motion recognition based on multipart sensors and wearable videos","award":["61602430"],"award-info":[{"award-number":["61602430"]}]},{"name":"The Pilot National Laboratory for Marine Science and Technology(Qingdao) Aoshan Science and Technology Innovation Project","award":["2016ASKJ07"],"award-info":[{"award-number":["2016ASKJ07"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As is known, cerebral stroke has become one of the main diseases endangering people\u2019s health; ischaemic strokes accounts for approximately 85% of cerebral strokes. According to research, early prediction and prevention can effectively reduce the incidence rate of the disease. However, it is difficult to predict the ischaemic stroke because the data related to the disease are multi-modal. To achieve high accuracy of prediction and combine the stroke risk predictors obtained by previous researchers, a method for predicting the probability of stroke occurrence based on a multi-model fusion convolutional neural network structure is proposed. In such a way, the accuracy of ischaemic stroke prediction is improved by processing multi-modal data through multiple end-to-end neural networks. In this method, the feature extraction of structured data (age, gender, history of hypertension, etc.) and streaming data (heart rate, blood pressure, etc.) based on a convolutional neural network is first realized. A neural network model for feature fusion is then constructed to realize the feature fusion of structured data and streaming data. Finally, a predictive model for predicting the probability of stroke is obtained by training. As shown in the experimental results, the accuracy of ischaemic stroke prediction reached 98.53%. Such a high prediction accuracy will be helpful for preventing the occurrence of stroke.<\/jats:p>","DOI":"10.3390\/s20174995","type":"journal-article","created":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T08:40:26Z","timestamp":1599122426000},"page":"4995","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model"],"prefix":"10.3390","volume":"20","author":[{"given":"Yan","family":"Liu","sequence":"first","affiliation":[{"name":"College of information science and engineering, Ocean University of China, Qingdao 266000, China"}]},{"given":"Bo","family":"Yin","sequence":"additional","affiliation":[{"name":"College of information science and engineering, Ocean University of China, Qingdao 266000, China"},{"name":"Pilot National Laboratory for Marine Science and Technology, Qingdao 266000, China"}]},{"given":"Yanping","family":"Cong","sequence":"additional","affiliation":[{"name":"College of information science and engineering, Ocean University of China, Qingdao 266000, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"259","DOI":"10.2147\/NDT.S128807","article-title":"Role of prediabetes in stroke","volume":"13","author":"Mijajlovic","year":"2017","journal-title":"Neuropsychiatr. 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