{"title":"Application of Machine Learning techniques to improve detection, diagnosis & prediction of breast cancer: A Comparative Analysis","authors":"Shobhit Shrotriya, Nizar Banu P K, Avi Kulkarni","doi":"10.37022/jiaps.v8i3.519","DOIUrl":null,"url":null,"abstract":"There is an increasing cancer burden in India across ages and sexes. The most significant cancer incident rate in females is ‘Breast Cancer’. Early detection and treatment are the key to lower mortality rate and better survival rates for cancer patients in the country. This review paper provides an understanding of the various types of breast cancers, their symptoms, causes, current detection and diagnosis methods. The paper presents different Machine Learning (ML) techniques that are in development for the detection and diagnosis of breast cancer. The objective of the paper is to highlight outcomes of some select previous studies between 2016 to 2020 using various ML techniques and summarize the selected algorithms which can be used for breast cancer prediction and diagnosis. In our paper, we have also made an attempt to implement the Convolutional Neural Networks (CNN) model on the Breast Cancer Wisconsin (Diagnostic) dataset, whose results are presented and discussed.","PeriodicalId":151037,"journal":{"name":"Journal of Innovations in Applied Pharmaceutical Science (JIAPS)","volume":"86 10","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Innovations in Applied Pharmaceutical Science (JIAPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37022/jiaps.v8i3.519","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
There is an increasing cancer burden in India across ages and sexes. The most significant cancer incident rate in females is ‘Breast Cancer’. Early detection and treatment are the key to lower mortality rate and better survival rates for cancer patients in the country. This review paper provides an understanding of the various types of breast cancers, their symptoms, causes, current detection and diagnosis methods. The paper presents different Machine Learning (ML) techniques that are in development for the detection and diagnosis of breast cancer. The objective of the paper is to highlight outcomes of some select previous studies between 2016 to 2020 using various ML techniques and summarize the selected algorithms which can be used for breast cancer prediction and diagnosis. In our paper, we have also made an attempt to implement the Convolutional Neural Networks (CNN) model on the Breast Cancer Wisconsin (Diagnostic) dataset, whose results are presented and discussed.