Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313547030> ?p ?o ?g. }
- W4313547030 endingPage "107340" @default.
- W4313547030 startingPage "107340" @default.
- W4313547030 abstract "Since the early symptoms of chronic obstructive pulmonary disease (COPD) are not obvious, patients are not easily identified, causing improper time for prevention and treatment. In present study, machine learning (ML) methods were employed to construct a risk prediction model for COPD to improve its prediction efficiency. We collected data from a sample of 5807 cases with a complete COPD diagnosis from the 2019 COPD Surveillance Program in Shanxi Province and extracted 34 potentially relevant variables from the dataset. Firstly, we used feature selection methods (i.e., Generalized elastic net, Lasso and Adaptive lasso) to select ten variables. Afterwards, we employed supervised classifiers for class imbalanced data by combining the cost-sensitive learning and SMOTE resampling methods with the ML methods (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, NGBoost and Stacking), respectively. Last, we assessed their performance. The cough frequently at age 14 and before and other 9 variables are significant parameters for COPD. The Stacking heterogeneous ensemble model showed relatively good performance in the unbalanced datasets. The Logistic Regression with class weighting enjoyed the best classification performance in the balancing data when these composite indicators (AUC, F1-Score and G-mean) were used as criteria for model comparison. The values of F1-Score and G-mean for the top three ML models were 0.290/0.660 for Logistic Regression with class weighting, 0.288/0.649 for Stacking with synthetic minority oversampling technique (SMOTE), and 0.285/0.648 for LightGBM with SMOTE. This paper combining feature selection methods, unbalanced data processing methods and machine learning methods with data from disease surveillance questionnaires and physical measurements to identify people at risk of COPD, concluded that machine learning models based on survey questionnaires could provide an automated identification for patients at risk of COPD, and provide a simple and scientific aid for early identification of COPD." @default.
- W4313547030 created "2023-01-06" @default.
- W4313547030 creator A5003939755 @default.
- W4313547030 creator A5016525537 @default.
- W4313547030 creator A5028294527 @default.
- W4313547030 creator A5034821104 @default.
- W4313547030 creator A5061916655 @default.
- W4313547030 creator A5064507780 @default.
- W4313547030 creator A5067370939 @default.
- W4313547030 creator A5069919632 @default.
- W4313547030 creator A5070793366 @default.
- W4313547030 creator A5071560359 @default.
- W4313547030 creator A5077408825 @default.
- W4313547030 creator A5086304494 @default.
- W4313547030 date "2023-03-01" @default.
- W4313547030 modified "2023-10-18" @default.
- W4313547030 title "Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data" @default.
- W4313547030 cites W1602160603 @default.
- W4313547030 cites W1879406984 @default.
- W4313547030 cites W1968888496 @default.
- W4313547030 cites W1980500788 @default.
- W4313547030 cites W2018060680 @default.
- W4313547030 cites W2020925091 @default.
- W4313547030 cites W2040388622 @default.
- W4313547030 cites W2045616259 @default.
- W4313547030 cites W2060947741 @default.
- W4313547030 cites W2107395400 @default.
- W4313547030 cites W2111374619 @default.
- W4313547030 cites W2121866145 @default.
- W4313547030 cites W2127237061 @default.
- W4313547030 cites W2143337123 @default.
- W4313547030 cites W2150981328 @default.
- W4313547030 cites W2331357155 @default.
- W4313547030 cites W2475055502 @default.
- W4313547030 cites W2516242839 @default.
- W4313547030 cites W2516938563 @default.
- W4313547030 cites W2770001088 @default.
- W4313547030 cites W2798095897 @default.
- W4313547030 cites W28412257 @default.
- W4313547030 cites W2913997948 @default.
- W4313547030 cites W2994586973 @default.
- W4313547030 cites W3004808374 @default.
- W4313547030 cites W3011484826 @default.
- W4313547030 cites W3150832616 @default.
- W4313547030 cites W3206964737 @default.
- W4313547030 cites W4239510810 @default.
- W4313547030 doi "https://doi.org/10.1016/j.cmpb.2023.107340" @default.
- W4313547030 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36640604" @default.
- W4313547030 hasPublicationYear "2023" @default.
- W4313547030 type Work @default.
- W4313547030 citedByCount "4" @default.
- W4313547030 countsByYear W43135470302023 @default.
- W4313547030 crossrefType "journal-article" @default.
- W4313547030 hasAuthorship W4313547030A5003939755 @default.
- W4313547030 hasAuthorship W4313547030A5016525537 @default.
- W4313547030 hasAuthorship W4313547030A5028294527 @default.
- W4313547030 hasAuthorship W4313547030A5034821104 @default.
- W4313547030 hasAuthorship W4313547030A5061916655 @default.
- W4313547030 hasAuthorship W4313547030A5064507780 @default.
- W4313547030 hasAuthorship W4313547030A5067370939 @default.
- W4313547030 hasAuthorship W4313547030A5069919632 @default.
- W4313547030 hasAuthorship W4313547030A5070793366 @default.
- W4313547030 hasAuthorship W4313547030A5071560359 @default.
- W4313547030 hasAuthorship W4313547030A5077408825 @default.
- W4313547030 hasAuthorship W4313547030A5086304494 @default.
- W4313547030 hasBestOaLocation W43135470301 @default.
- W4313547030 hasConcept C119857082 @default.
- W4313547030 hasConcept C12267149 @default.
- W4313547030 hasConcept C124101348 @default.
- W4313547030 hasConcept C126322002 @default.
- W4313547030 hasConcept C126838900 @default.
- W4313547030 hasConcept C136764020 @default.
- W4313547030 hasConcept C148483581 @default.
- W4313547030 hasConcept C151956035 @default.
- W4313547030 hasConcept C154945302 @default.
- W4313547030 hasConcept C169258074 @default.
- W4313547030 hasConcept C183115368 @default.
- W4313547030 hasConcept C203868755 @default.
- W4313547030 hasConcept C2776780178 @default.
- W4313547030 hasConcept C37616216 @default.
- W4313547030 hasConcept C41008148 @default.
- W4313547030 hasConcept C71924100 @default.
- W4313547030 hasConceptScore W4313547030C119857082 @default.
- W4313547030 hasConceptScore W4313547030C12267149 @default.
- W4313547030 hasConceptScore W4313547030C124101348 @default.
- W4313547030 hasConceptScore W4313547030C126322002 @default.
- W4313547030 hasConceptScore W4313547030C126838900 @default.
- W4313547030 hasConceptScore W4313547030C136764020 @default.
- W4313547030 hasConceptScore W4313547030C148483581 @default.
- W4313547030 hasConceptScore W4313547030C151956035 @default.
- W4313547030 hasConceptScore W4313547030C154945302 @default.
- W4313547030 hasConceptScore W4313547030C169258074 @default.
- W4313547030 hasConceptScore W4313547030C183115368 @default.
- W4313547030 hasConceptScore W4313547030C203868755 @default.
- W4313547030 hasConceptScore W4313547030C2776780178 @default.
- W4313547030 hasConceptScore W4313547030C37616216 @default.
- W4313547030 hasConceptScore W4313547030C41008148 @default.
- W4313547030 hasConceptScore W4313547030C71924100 @default.