{"title":"乳腺癌风险评估模型的应用","authors":"Huaizhou Yang, Tian Luo, Chenzhuo Liu","doi":"10.1109/ICSP54964.2022.9778357","DOIUrl":null,"url":null,"abstract":"Looking for a risk assessment model is of great significance for predicting, preventing and diagnosing breast cancer. This paper collects relevant data from SEER database(Survey, Epidemiology, and End Results), uses SVM (Support Vector Machine) and random forest in data mining to predict the possibility of breast cancer, and discusses the application value of Gail breast cancer risk assessment model. Finally, the prediction results based on three risk assessment models are analyzed. The analysis results show that the prediction accuracy rate of Gail model is more excellent.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Risk Assessment Model for Breast Cancer\",\"authors\":\"Huaizhou Yang, Tian Luo, Chenzhuo Liu\",\"doi\":\"10.1109/ICSP54964.2022.9778357\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Looking for a risk assessment model is of great significance for predicting, preventing and diagnosing breast cancer. This paper collects relevant data from SEER database(Survey, Epidemiology, and End Results), uses SVM (Support Vector Machine) and random forest in data mining to predict the possibility of breast cancer, and discusses the application value of Gail breast cancer risk assessment model. Finally, the prediction results based on three risk assessment models are analyzed. The analysis results show that the prediction accuracy rate of Gail model is more excellent.\",\"PeriodicalId\":363766,\"journal\":{\"name\":\"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSP54964.2022.9778357\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSP54964.2022.9778357","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
寻找乳腺癌的风险评估模型对乳腺癌的预测、预防和诊断具有重要意义。本文从SEER数据库(Survey, Epidemiology, and End Results)中收集相关数据,利用数据挖掘中的SVM(支持向量机)和随机森林对乳腺癌的可能性进行预测,并探讨Gail乳腺癌风险评估模型的应用价值。最后,对基于三种风险评估模型的预测结果进行了分析。分析结果表明,Gail模型的预测准确率更高。
Application of Risk Assessment Model for Breast Cancer
Looking for a risk assessment model is of great significance for predicting, preventing and diagnosing breast cancer. This paper collects relevant data from SEER database(Survey, Epidemiology, and End Results), uses SVM (Support Vector Machine) and random forest in data mining to predict the possibility of breast cancer, and discusses the application value of Gail breast cancer risk assessment model. Finally, the prediction results based on three risk assessment models are analyzed. The analysis results show that the prediction accuracy rate of Gail model is more excellent.