{"title":"基于机器学习的乳腺癌诊断框架","authors":"Ravi Kumar Sachdeva, Priyanka Bathla","doi":"10.4018/ijsi.301221","DOIUrl":null,"url":null,"abstract":"Machine learning is used in the health care sector due to its ability to make predictions. Nowadays major cause of death in women is due to breast cancer. In this paper, a machine learning-based framework for the diagnosis of breast cancer has been proposed. The authors have used different feature selection methods on Breast Cancer Wisconsin (Diagnostic) dataset i.e. Chi-square, Pearson correlation between features and Feature importance. The competency of the feature selection methods has been analyzed using different machine learning classifiers on different performance parameters like accuracy, sensitivity, specificity, precision, and F-measure. Random Forest (RF), Extra Tree Classifier (ETC), and Logistic Regression (LR) machine learning classifiers have been used by the authors. Results reveal that FI (Feature Importance) is the preeminent feature selection method among all others used when applied with different classifiers. Results also show that the ETC machine learning classifier gives the best accuracy result in comparison with RF and LR classifiers.","PeriodicalId":396598,"journal":{"name":"Int. J. Softw. Innov.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A Machine Learning-Based Framework for Diagnosis of Breast Cancer\",\"authors\":\"Ravi Kumar Sachdeva, Priyanka Bathla\",\"doi\":\"10.4018/ijsi.301221\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning is used in the health care sector due to its ability to make predictions. Nowadays major cause of death in women is due to breast cancer. In this paper, a machine learning-based framework for the diagnosis of breast cancer has been proposed. The authors have used different feature selection methods on Breast Cancer Wisconsin (Diagnostic) dataset i.e. Chi-square, Pearson correlation between features and Feature importance. The competency of the feature selection methods has been analyzed using different machine learning classifiers on different performance parameters like accuracy, sensitivity, specificity, precision, and F-measure. Random Forest (RF), Extra Tree Classifier (ETC), and Logistic Regression (LR) machine learning classifiers have been used by the authors. Results reveal that FI (Feature Importance) is the preeminent feature selection method among all others used when applied with different classifiers. Results also show that the ETC machine learning classifier gives the best accuracy result in comparison with RF and LR classifiers.\",\"PeriodicalId\":396598,\"journal\":{\"name\":\"Int. J. Softw. Innov.\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Softw. Innov.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijsi.301221\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Softw. Innov.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijsi.301221","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Machine Learning-Based Framework for Diagnosis of Breast Cancer
Machine learning is used in the health care sector due to its ability to make predictions. Nowadays major cause of death in women is due to breast cancer. In this paper, a machine learning-based framework for the diagnosis of breast cancer has been proposed. The authors have used different feature selection methods on Breast Cancer Wisconsin (Diagnostic) dataset i.e. Chi-square, Pearson correlation between features and Feature importance. The competency of the feature selection methods has been analyzed using different machine learning classifiers on different performance parameters like accuracy, sensitivity, specificity, precision, and F-measure. Random Forest (RF), Extra Tree Classifier (ETC), and Logistic Regression (LR) machine learning classifiers have been used by the authors. Results reveal that FI (Feature Importance) is the preeminent feature selection method among all others used when applied with different classifiers. Results also show that the ETC machine learning classifier gives the best accuracy result in comparison with RF and LR classifiers.