Model Comparison for Breast Cancer Prognosis Based on Clinical Data
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{"title"=>"Model comparison for breast cancer prognosis based on clinical data", "type"=>"journal", "authors"=>[{"first_name"=>"Sabri", "last_name"=>"Boughorbel", "scopus_author_id"=>"6504801269"}, {"first_name"=>"Rashid", "last_name"=>"Al-Ali", "scopus_author_id"=>"57190417992"}, {"first_name"=>"Naser", "last_name"=>"Elkum", "scopus_author_id"=>"24314984100"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84955448634", "pmid"=>"26771838", "pui"=>"607807400", "scopus"=>"2-s2.0-84955448634", "doi"=>"10.1371/journal.pone.0146413", "issn"=>"19326203"}, "id"=>"d8f7aaca-e537-31fb-b041-325f2b1457d6", "abstract"=>"We compared the performance of several prediction techniques for breast cancer prognosis, based on AU-ROC performance (Area Under ROC) for different prognosis periods. The analyzed dataset contained 1,981 patients and from an initial 25 variables, the 11 most common clinical predictors were retained. We compared eight models from a wide spectrum of predictive models, namely; Generalized Linear Model (GLM), GLM-Net, Partial Least Square (PLS), Support Vector Machines (SVM), Random Forests (RF), Neural Networks, k-Nearest Neighbors (k-NN) and Boosted Trees. In order to compare these models, paired t-test was applied on the model performance differences obtained from data resampling. Random Forests, Boosted Trees, Partial Least Square and GLMNet have superior overall performance, however they are only slightly higher than the other models. The comparative analysis also allowed us to define a relative variable importance as the average of variable importance from the different models. Two sets of variables are identified from this analysis. The first includes number of positive lymph nodes, tumor size, cancer grade and estrogen receptor, all has an important influence on model predictability. The second set incudes variables related to histological parameters and treatment types. The short term vs long term contribution of the clinical variables are also analyzed from the comparative models. From the various cancer treatment plans, the combination of Chemo/Radio therapy leads to the largest impact on cancer prognosis.", "link"=>"http://www.mendeley.com/research/model-comparison-breast-cancer-prognosis-based-clinical-data-1", "reader_count"=>22, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>4, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>2, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>4, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>2, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>4, "Nursing and Health Professions"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>5, "Neuroscience"=>1, "Physics and Astronomy"=>1, "Computer Science"=>4, "Agricultural and Biological Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Neuroscience"=>{"Neuroscience"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Computer Science"=>{"Computer Science"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"United States"=>1}, "group_count"=>4}

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Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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