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Impact of data balancing a multiclass dataset before the creation of association rules to study bacterial vaginosis

Intelligent Medicine 2024年第3期4卷 188-199页

作者： Freddy de la Cruz-Ruiz Juana Canul-Reich Rafael Rivera-López Erick de la Cruz-Hernández Academic Division of Science and Technology of the Information Juárez Autonomous University of TabascoLa Esmeralda DistrictTabascoMexico Department of Systems National Technological Institute of MexicoTechnological Institute of VeracruzVeracruzMexico Multidisciplinary Academic Division of Comalcalco Juárez Autonomous University of TabascoTabascoMexico

Background Bacterial vaginosis is a polymicrobial syndrome in which the homeostasis exerted by the Latobacillus species that protect the vaginal mucosa has been *** study explored the data balancing process with the intention of improving the quality of association *** article aimed to balance the unbalanced multiclass dataset to improve association rule *** A dataset with 201 observations and 58 variables was analyzed.A preconstructed dataset was *** authors collected the data between August 2016 and October 2018 in Tabasco,*** study population comprised sexually active women ages 18 to 50 who underwent gynecological inspection at the infectious and metabolic diseases research laboratory at the Universidad Juarez Autonoma de *** determine the best κ-value,the random-forest algorithm was used and the balancing was performed with the synthetic minority over-sampling technique(SMOTE),random over-sampling examples(ROSE),and adaptive syntetic sampling approach for imbalanced learning(ADASYN)*** Apriori algorithm created the rules and to select rules with statistical significance,the ***(),***(),and ***()functions and quality metric Fisher’s exact tes were *** biological validation was carried out by the expert(bacteriologist).Results The ADASYN algorithm at K=9 the out of the bag(OOB)error was zero,this was the best𝐾-*** the balancing process the ADASYN algorithm show best the *** the dataset balanced with ADASYN,the apriori algorithm created the association rules and the selection with the quality metric Fisher’s exact test,and the biological validation reported 13 ***-bacteria Atopobium vaginae,Gardnerella vaginalis,Megasphaera filotipo 1,Mycoplasma hominis and Ureaplasma parvum were detected by the apriori algorithm from the balanced *** balancing may improve the creation of association rules to efficiently model the bacteria that cause bac

关键词： Bacterial vaginosis data balancing Random forest Synthetic minority over-sampling technique

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A Model to Predict Cancer Comorbid Patient’s Survivability Based on Improved Chi2 Model

A Model to Predict Cancer Comorbid Patient’s Survivability ...

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第三十九届中国控制会议

作者： Peng Liu Shumin Fei Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University

Modeling of Cancer comorbid patients’ survival probability has theoretical significance and practical needs. Cancer survivability prediction may provide advices for clinical decisions and personalized medicine. Surveillance, Epidemiology, and End Results Program（SEER） provides large data sets to be analyzed with machine learning methods. In this study, male and female cancer comorbid cases（male-genital and urinal for men and breast and female-genital for women） are identified and labeled from the SEER database;the data set is then processed with improved Chi2 test based feature selection and random undersampling based data-balancing;lightGBM is adopted as the classifier. The results indicate that the proposed method is effective and generalizable.

关键词： Cancer Comorbidity Survival Prediction LightGBM Chi2 model data balancing

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