A New Hybrid Feature Selection Sequence for Predicting Breast Cancer Survivability Using Clinical Datasets

作     者：E.Jenifer Sweetlin S.Saudia 

作者机构：Centre for Information Technology and EngineeringManonmaniam Sundaranar UniversityTirunelveliIndia 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第7期

页      面：343-367页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

主　　题：Accuracy feature selection filter methods ML-based classifiers wrapper methods 

摘      要：This paper proposes a hybrid feature selection sequence comple-mented with filter and wrapper concepts to improve the accuracy of Machine Learning(ML)based supervised classifiers for classifying the survivability of breast cancer patients into classes,living and deceased using METABRIC and Surveillance,Epidemiology and End Results(SEER)*** ML-based classifiers used in the analysis are:Multiple Logistic Regression,K-Nearest Neighbors,Decision Tree,Random Forest,Support Vector Machine and Multilayer *** workflow of the proposed ML algorithm sequence comprises the following stages:data cleaning,data balancing,feature selection via a filter and wrapper sequence,cross validation-based training,testing and performance *** results obtained are compared in terms of the following classification metrics:Accuracy,Precision,F1 score,True Positive Rate,True Negative Rate,False Positive Rate,False Negative Rate,Area under the Receiver Operating Characteristics curve,Area under the Precision-Recall curve and Mathews Correlation *** comparison shows that the proposed feature selection sequence produces better results from all supervised classifiers than all other feature selection sequences considered in the analysis.