{"title":"基于决策树方法的ML乳腺癌分级","authors":"Divya Paikaray, G. Jethava","doi":"10.1109/SMART55829.2022.10047004","DOIUrl":null,"url":null,"abstract":"BC's high mortality and morbidity rates endanger female patients. Thus, a breast cancer detection method is essential. Logistic regression, DTs, random forests, and CNN predicted breast cancer. Predicting early breast cancer symptoms requires ML. This study uses three classification ML techniques. We'll evaluate each algorithm's performance and accuracy. Classification systems must carefully manage and preprocess unbalanced data. We'll train ML models on BC patient data. Performance and accuracy comparisons identify the best algorithm for this task. This study will compare BC classification models to determine the optimal approach. This study predicts BC classification system accuracy.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ML based with Decision Tree Method for Classifying The Breast Cancer Level\",\"authors\":\"Divya Paikaray, G. Jethava\",\"doi\":\"10.1109/SMART55829.2022.10047004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BC's high mortality and morbidity rates endanger female patients. Thus, a breast cancer detection method is essential. Logistic regression, DTs, random forests, and CNN predicted breast cancer. Predicting early breast cancer symptoms requires ML. This study uses three classification ML techniques. We'll evaluate each algorithm's performance and accuracy. Classification systems must carefully manage and preprocess unbalanced data. We'll train ML models on BC patient data. Performance and accuracy comparisons identify the best algorithm for this task. This study will compare BC classification models to determine the optimal approach. This study predicts BC classification system accuracy.\",\"PeriodicalId\":431639,\"journal\":{\"name\":\"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMART55829.2022.10047004\",\"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 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMART55829.2022.10047004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ML based with Decision Tree Method for Classifying The Breast Cancer Level
BC's high mortality and morbidity rates endanger female patients. Thus, a breast cancer detection method is essential. Logistic regression, DTs, random forests, and CNN predicted breast cancer. Predicting early breast cancer symptoms requires ML. This study uses three classification ML techniques. We'll evaluate each algorithm's performance and accuracy. Classification systems must carefully manage and preprocess unbalanced data. We'll train ML models on BC patient data. Performance and accuracy comparisons identify the best algorithm for this task. This study will compare BC classification models to determine the optimal approach. This study predicts BC classification system accuracy.