{"title":"区间目标配置方法在心血管疾病诊断神经网络分类器综合问题中的应用","authors":"E. Mirkin, E. Savchenko, E. Savchenko","doi":"10.1109/EExPolytech50912.2020.9243859","DOIUrl":null,"url":null,"abstract":"A new approach of using the paradigm of “interval teacher” in the classical scheme of training neural networks with a teacher is used to solve the problem of medical diagnosis of the disease coronary heart disease. An NN architecture has been created that implements a mechanism for selecting a specific “teacher” from a deterministic interval set. A comparative analysis of the proposed and classical training schemes of the neural network is shown, confirming the effectiveness of the proposed training concept. The created medical classifier based on a neural network with an interval teacher provides high classification accuracy.","PeriodicalId":374410,"journal":{"name":"2020 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Use of the Method of Configuring the Interval Target in the Problem of Synthesis of the Neural Network Classifier for Diagnosing Cardiovascular Diseases\",\"authors\":\"E. Mirkin, E. Savchenko, E. Savchenko\",\"doi\":\"10.1109/EExPolytech50912.2020.9243859\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new approach of using the paradigm of “interval teacher” in the classical scheme of training neural networks with a teacher is used to solve the problem of medical diagnosis of the disease coronary heart disease. An NN architecture has been created that implements a mechanism for selecting a specific “teacher” from a deterministic interval set. A comparative analysis of the proposed and classical training schemes of the neural network is shown, confirming the effectiveness of the proposed training concept. The created medical classifier based on a neural network with an interval teacher provides high classification accuracy.\",\"PeriodicalId\":374410,\"journal\":{\"name\":\"2020 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech)\",\"volume\":\"78 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EExPolytech50912.2020.9243859\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EExPolytech50912.2020.9243859","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Use of the Method of Configuring the Interval Target in the Problem of Synthesis of the Neural Network Classifier for Diagnosing Cardiovascular Diseases
A new approach of using the paradigm of “interval teacher” in the classical scheme of training neural networks with a teacher is used to solve the problem of medical diagnosis of the disease coronary heart disease. An NN architecture has been created that implements a mechanism for selecting a specific “teacher” from a deterministic interval set. A comparative analysis of the proposed and classical training schemes of the neural network is shown, confirming the effectiveness of the proposed training concept. The created medical classifier based on a neural network with an interval teacher provides high classification accuracy.