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Chi-Square and PCA Based Feature Selection for Diabetes Detection with ensemble classifier

Intelligent Automation & Soft Computing 2023年第5期36卷 1931-1949页

作者： Vaibhav Rupapara Furqan Rustam Abid Ishaq ernesto Lee Imran Ashraf School of Computing and Information Sciences Florida International UniversityUSA Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Computer Science Broward CollegeBroward CountyFloridaUSA Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health *** the last few years,an alarming increase is observed worldwide with a 70%rise in the disease since 2000 and an 80%rise in male *** untreated,it results in complications of many vital organs of the human body which may lead to *** detection of diabetes is a task of signiﬁcant importance to start timely *** study introduces a methodology for the classiﬁcation of diabetic and normal people using an ensemble machine learning model and feature fusion of Chi-square and principal component *** ensemble model,logistic tree classiﬁer(LTC),is proposed which incorporates logistic regression and extra tree classiﬁer through a soft voting *** are also performed using several well-known machine learning algorithms to analyze their performance including logistic regression,extra tree classiﬁer,AdaBoost,Gaussian naive Bayes,decision tree,random forest,and k nearest *** addition,several experiments are carried out using principal component analysis(PCA)and Chi-square(Chi-2)fea-tures to analyze the inﬂuence of feature selection on the performance of machine learning classiﬁ*** indicate that Chi-2 features show high performance than both PCA features and original ***,the highest accuracy is obtained when the proposed ensemble model LTC is used with the proposed fea-ture fusion framework-work which achieves a 0.85 accuracy score which is the highest of the available approaches for diabetes *** addition,the statis-tical T-test proves the statistical signiﬁcance of the proposed approach over other approaches.

关键词： Diabetes mellitus prediction feature fusion ensemble classiﬁer principal component analysis chi-square

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classifying Hematoxylin and Eosin Images Using a Super-Resolution Segmentor and a Deep ensemble classifier

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Intelligent Automation & Soft Computing 2023年第8期37卷 1983-2000页

作者： P.Sabitha G.Meeragandhi Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiTamilnaduIndia

Developing an automatic and credible diagnostic system to analyze the type,stage,and level of the liver cancer from Hematoxylin and Eosin(H&E)images is a very challenging and time-consuming endeavor,even for experienced pathologists,due to the non-uniform illumination and *** several Machine Learning(ML)and Deep Learning(DL)approaches are employed to increase the performance of automatic liver cancer diagnostic systems,the classi-ﬁcation accuracy of these systems still needs signiﬁcant improvement to satisfy the real-time requirement of the diagnostic *** this work,we present a new ensemble classiﬁer(hereafter called ECNet)to classify the H&E stained liver histopathology images *** proposed model employs a Dropout Extreme Learning Machine(DrpXLM)and the Enhanced Convolutional Block Attention Modules(ECBAM)based residual *** applies Voting Mechanism(VM)to integrate the decisions of individual classiﬁers using the average of probabilities ***,the nuclei regions in the H&E stain are seg-mented through Super-resolution Convolutional Networks(SrCN),and then these regions are fed into the ensemble DL network for classiﬁ*** effectiveness of the proposed model is carefully studied on real-world *** results of our meticulous experiments on the Kasturba Medical College(KMC)liver dataset reveal that the proposed ECNet signiﬁcantly outperforms other existing classiﬁca-tion networks with better accuracy,sensitivity,speciﬁcity,precision,and Jaccard Similarity Score(JSS)of 96.5%,99.4%,89.7%,95.7%,and 95.2%,*** obtain similar results from ECNet when applied to The Cancer Genome Atlas Liver Hepatocellular Carcinoma(TCGA-LIHC)dataset regarding accuracy(96.3%),sensitivity(97.5%),speciﬁcity(93.2%),precision(97.5%),and JSS(95.1%).More importantly,the proposed ECNet system consumes only 12.22 s for training and 1.24 s for ***,we carry out the Wilcoxon statistical test to determine whether the ECN

关键词： Convolutional block attention modules dropout ELM ensemble classiﬁer liver cancer segmentation voting mechanism

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