{"doi":"10.1109/embc.2019.8856582","title":"Feature Selection Algorithm based on Random Forest applied to Sleep Apnea Detection","abstract":null,"journal":"2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","year":2019,"id":46397,"datarank":1.4215209773638404,"base_score":3.295836866004329,"endowment":3.295836866004329,"self_citation_contribution":0.4943755299006494,"citation_network_contribution":0.927145447463191,"self_endowment_contribution":0.4943755299006494,"citer_contribution":0.927145447463191,"corpus_percentile":null,"corpus_rank":null,"citation_count":26,"citer_count":20,"citers_with_citation_signal":17,"citers_with_endowment":17,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":215244,"name":"Dries Testelmans","orcid":null,"position":1,"is_corresponding":false},{"id":215245,"name":"Pascal Borzee","orcid":null,"position":2,"is_corresponding":false},{"id":215246,"name":"Bertien Buyse","orcid":null,"position":3,"is_corresponding":false},{"id":215247,"name":"Sabine Van Huffel","orcid":null,"position":4,"is_corresponding":false},{"id":215248,"name":"Carolina Varon","orcid":null,"position":5,"is_corresponding":false},{"id":215243,"name":"Margot Deviaene","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Feature Selection Algorithm based on Random Forest applied to Sleep Apnea Detection","abstract":"This paper presents a new feature selection method based on the changes in out-of-bag (OOB) Cohen kappa values of a random forest (RF) classifier, which was tested on the automatic detection of sleep apnea based on the oxygen saturation signal (SpO <sub xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sub> ). The feature selection method is based on the RF predictor importance defined as the increase in error when features are permuted. This method is improved by changing the classification error into the Cohen kappa value, by adding an extra factor to avoid correlated features and by adapting the OOB sample selection to obtain a patient independent validation. When applying the method for sleep apnea classification, an optimal feature set of 3 parameters was selected out of 286. This was half of the 6 features that were obtained in our previous study. This feature reduction resulted in an improved interpretability of our model, but also a slight decrease in performance, without affecting the clinical screening performance. Feature selection is an important issue in machine learning and especially biomedical informatics. This new feature selection method introduces interesting improvements of RF feature selection methods, which can lead to a reduced feature set and an improved classifier interpretability.","is_dataset_classified":null,"base_score":3.295836866004329,"endowment":3.295836866004329,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"31946424","pmcid":null,"openalex_id":"https://openalex.org/W2980082554","authors":[],"funders":[],"total_grants":0,"fwci":2.0676,"citation_percentile":0.87547499,"influential_citations":1,"citation_trend":[{"year":2019,"count":4},{"year":2021,"count":7},{"year":2022,"count":5},{"year":2023,"count":3},{"year":2024,"count":5},{"year":2025,"count":2}],"oa_status":"green","license":"other-oa","oa_locations":[{"url":"https://lirias.kuleuven.be/retrieve/e067a194-85c5-4ec7-bd58-f9193dfde697","host_type":"repository"},{"url":"https://lirias.kuleuven.be/bitstream/123456789/641685/2/19-22.pdf","host_type":"GREEN"},{"url":"https://lirias.kuleuven.be/retrieve/e067a194-85c5-4ec7-bd58-f9193dfde697","host_type":"repository"},{"url":"http://xplorestaging.ieee.org/ielx7/8844528/8856280/08856582.pdf?arnumber=8856582","host_type":"publisher"},{"url":"https://lirias.kuleuven.be/handle/123456789/641685","host_type":"repository"},{"url":"https://doi.org/10.1109/embc.2019.8856582","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/31946424","host_type":"repository"}],"fields_of_study":["Obstructive Sleep Apnea Research","Neuroscience of respiration and sleep","Advanced Chemical Sensor Technologies","Medicine","Computer Science","Algorithms","Humans","Machine Learning","Oximetry","Sleep Apnea Syndromes"],"mesh_terms":["Machine Learning","Algorithms","Humans","Oximetry","Sleep Apnea Syndromes"],"keywords":["Random forest","Feature selection","Interpretability","Artificial intelligence","Computer science","Pattern recognition (psychology)","Feature (linguistics)","Classifier (UML)","Machine learning","Feature extraction","Statistical classification","Data mining"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Life in Land"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-09T02:22:37.889795Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}