Arumbaka Srinivasa Rao, Yamini Tondepu, Siva Kumari N, Ch. Prasad
{"title":"An Early Detection of Breast Cancer Using Hybrid Ensemble Classifier","authors":"Arumbaka Srinivasa Rao, Yamini Tondepu, Siva Kumari N, Ch. Prasad","doi":"10.1109/I-SMAC52330.2021.9640795","DOIUrl":null,"url":null,"abstract":"In the past few years, India has reported 30% of breast cancer cases, and this number is likely to increase. In India, a woman is diagnosed with breast cancer every two minutes and dies every nine minutes. Women who are diagnosed and treated early can have a better chance for survival. This article offers a new machine learning-based strategy for diagnosing breast cancer known as an Enhanced ensembled classification model. Further, this research work has conducted an experimental analysis to check the validity of the dataset extracted from the Kaggle repository. When compared to other algorithms such as Logistic Regression and SVM, the proposed model provides more accurate and effective outcomes when implemented and compared with existing methods.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I-SMAC52330.2021.9640795","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In the past few years, India has reported 30% of breast cancer cases, and this number is likely to increase. In India, a woman is diagnosed with breast cancer every two minutes and dies every nine minutes. Women who are diagnosed and treated early can have a better chance for survival. This article offers a new machine learning-based strategy for diagnosing breast cancer known as an Enhanced ensembled classification model. Further, this research work has conducted an experimental analysis to check the validity of the dataset extracted from the Kaggle repository. When compared to other algorithms such as Logistic Regression and SVM, the proposed model provides more accurate and effective outcomes when implemented and compared with existing methods.