Classifying Big Medical Data through Bootstrap Decision Forest Using Penalizing Attributes

作     者：V.Gowri V.Vijaya Chamundeeswari 

作者机构：Department of CSESRM Institute of Science and TechnologyRamapuram CampusChennaiTamilnaduIndia Department of CSESaveetha Engineering CollegeChennaiTamilnaduIndia 

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

年 卷 期：2023年第36卷第6期

页      面：3675-3690页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Data classification decision forest feature selection healthcare data heart disease prediction penalizing attributes 

摘      要：Decision forest is a well-renowned machine learning technique to address the detection and prediction problems related to clinical ***,the tra-ditional decision forest(DF)algorithms have lower classification accuracy and cannot handle high-dimensional feature space *** this work,we pro-pose a bootstrap decision forest using penalizing attributes(BFPA)algorithm to predict heart disease with higher *** work integrates a significance-based attribute selection(SAS)algorithm with the BFPA classifier to improve the performance of the diagnostic system in identifying cardiac *** pro-posed SAS algorithm is used to determine the correlation among attributes and to select the optimum subset of feature space for learning and testing *** selects the optimal number of learning and testing data points as well as the density of trees in the forest to realize higher prediction accuracy in classifying imbalanced datasets *** effectiveness of the developed classifier is cautiously verified on the real-world database(i.e.,Heart disease dataset from UCI repository)by relating its enactment with many advanced approaches with respect to the accuracy,sensitivity,specificity,precision,and intersection over-union(IoU).The empirical results demonstrate that the intended classification approach outdoes other approaches with superior enactment regarding the accu-racy,precision,sensitivity,specificity,and IoU of 94.7%,99.2%,90.1%,91.1%,and 90.4%,***,we carry out Wilcoxon’s rank-sum test to determine whether our proposed classifier with feature selection method enables a noteworthy enhancement related to other classifiers or *** the experimental results,we can conclude that the integration of SAS and BFPA outperforms other classifiers recently reported in the literature.