Improving Case-Based Reasoning Systems by Combining K-Nearest Neighbour Algorithm with Logistic Regression in the Prediction of Patients’ Registration on the Renal Transplant Waiting List
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{"title"=>"Improving Case-Based Reasoning Systems by Combining K-Nearest Neighbour Algorithm with Logistic Regression in the Prediction of Patients' Registration on the Renal Transplant Waiting List", "type"=>"journal", "authors"=>[{"first_name"=>"Boris", "last_name"=>"Campillo-Gimenez", "scopus_author_id"=>"51565776900"}, {"first_name"=>"Wassim", "last_name"=>"Jouini", "scopus_author_id"=>"57193820820"}, {"first_name"=>"Sahar", "last_name"=>"Bayat", "scopus_author_id"=>"24330524900"}, {"first_name"=>"Marc", "last_name"=>"Cuggia", "scopus_author_id"=>"6507457425"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"369770264", "sgr"=>"84883631594", "doi"=>"10.1371/journal.pone.0071991", "arxiv"=>"1303.1700", "scopus"=>"2-s2.0-84883631594", "isbn"=>"1932-6203", "pmid"=>"24039730"}, "id"=>"1a5a67ad-8cbe-3a8d-bc0f-288bd1ea6652", "abstract"=>"INTRODUCTION: Case-based reasoning (CBR) is an emerging decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs) and a label (output). Extracting useful information from this database can help the CBR system providing more reliable results on the yet to be solved cases.\\n\\nOBJECTIVE: We suggest a general framework where a CBR system, viz. K-Nearest Neighbour (K-NN) algorithm, is combined with various information obtained from a Logistic Regression (LR) model, in order to improve prediction of access to the transplant waiting list.\\n\\nMETHODS: LR is applied, on the case database, to assign weights to the attributes as well as the solved cases. Thus, five possible decision making systems based on K-NN and/or LR were identified: a standalone K-NN, a standalone LR and three soft K-NN algorithms that rely on the weights based on the results of the LR. The evaluation was performed under two conditions, either using predictive factors known to be related to registration, or using a combination of factors related and not related to registration.\\n\\nRESULTS AND CONCLUSION: The results show that our suggested approach, where the K-NN algorithm relies on both weighted attributes and cases, can efficiently deal with non relevant attributes, whereas the four other approaches suffer from this kind of noisy setups. The robustness of this approach suggests interesting perspectives for medical problem solving tools using CBR methodology.", "link"=>"http://www.mendeley.com/research/improving-casebased-reasoning-systems-combining-knearest-neighbour-algorithm-logistic-regression-pre", "reader_count"=>25, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>1, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Environmental Science"=>1, "Medicine and Dentistry"=>4, "Design"=>1, "Computer Science"=>17}, "reader_count_by_subdiscipline"=>{"Design"=>{"Design"=>1}, "Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Computer Science"=>{"Computer Science"=>17}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"Brazil"=>1, "Portugal"=>1, "Spain"=>1}, "group_count"=>2}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/1198702"], "description"=>"<p>Predictions were performed by a logistic regression, a <i>K</i>-NN algorithm (<i>standalone CBR</i>), and three combinations of the <i>K</i>-NN algorithm with the logistic regression: <i>CBR+</i> - a <i>K</i>-NN with weighted attributes, <i>CBR+</i> - a <i>K</i>-NN with weighted patients, <i>CBR++</i> - a <i>K</i>-NN with both weightings of attributes and patients. Performances are presented in terms of bootstrap estimates of the aera under the ROC curve with 95% confidence intervals. Prediction before adding the 50 random variables, using either the complete available attributes of the case database (<b>A</b>), or only the attributes selected by a stepwise automatic selection procedure (<b>B</b>). Prediction after adding the 50 random variables, using either the complete available attributes of the case database (<b>C</b>), or only the variables selected by a stepwise automatic selection procedure (<b>D</b>).</p>", "links"=>[], "tags"=>["classification"], "article_id"=>795051, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Boris Campillo-Gimenez", "Wassim Jouini", "Sahar Bayat", "Marc Cuggia"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0071991.g002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performances_of_the_different_classification_algorithms_/795051", "title"=>"Performances of the different classification algorithms.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-09 03:05:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1198703"], "description"=>"*<p>at the first renal replacement therapy;</p>†<p>HCV: Hepatitis C Virus, HBV: Hepatitis B Virus, HIV: Human Immunodeficiency Virus.</p>", "links"=>[], "tags"=>["attributes", "weights", "neighbours", "algorithms", "adding", "50", "stepwise"], "article_id"=>795052, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Boris Campillo-Gimenez", "Wassim Jouini", "Sahar Bayat", "Marc Cuggia"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0071991.t001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_List_of_the_attributes_and_weights_used_by_the_K_Nearest_Neighbours_algorithms_before_and_after_adding_the_50_random_attributes_and_before_and_after_stepwise_selection_of_the_case_description_attributes_/795052", "title"=>"List of the attributes and weights used by the <i>K</i>-Nearest Neighbours algorithms before and after adding the 50 random attributes, and before and after stepwise selection of the case description attributes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-09 03:05:09"}

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Relative Metric

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