{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T21:54:09Z","timestamp":1740174849520,"version":"3.37.3"},"reference-count":20,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T00:00:00Z","timestamp":1638489600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81830052"],"award-info":[{"award-number":["81830052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003395","name":"Shanghai Municipal Education Commission","doi-asserted-by":"publisher","award":["81830052"],"award-info":[{"award-number":["81830052"]}],"id":[{"id":"10.13039\/501100003395","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004921","name":"Shanghai Jiao Tong University","doi-asserted-by":"publisher","award":["81830052"],"award-info":[{"award-number":["81830052"]}],"id":[{"id":"10.13039\/501100004921","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009002","name":"Shanghai University","doi-asserted-by":"publisher","award":["81830052"],"award-info":[{"award-number":["81830052"]}],"id":[{"id":"10.13039\/501100009002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"German UST Company","award":["81830052"],"award-info":[{"award-number":["81830052"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,12,3]]},"abstract":"<jats:p>Feature optimization, which is the theme of this paper, is actually the selective selection of the variables on the input side at the time of making a predictive kind of model. However, an improved feature optimization algorithm for breath signal based on the Pearson-BPSO was proposed and applied to distinguish hepatocellular carcinoma by electronic nose (eNose) in the paper. First, the multidimensional features of the breath curves of hepatocellular carcinoma patients and healthy controls in the training samples were extracted; then, the features with less relevance to the classification were removed according to the Pearson correlation coefficient; next, the fitness function was constructed based on K-Nearest Neighbor (KNN) classification error and feature dimension, and the feature optimization transformation matrix was obtained based on BPSO. Furthermore, the transformation matrix was applied to optimize the test sample\u2019s features. Finally, the performance of the optimization algorithm was evaluated by the classifier. The experiment results have shown that the Pearson-BPSO algorithm could effectively improve the classification performance compared with BPSO and PCA optimization methods. The accuracy of SVM and RF classifier was 86.03% and 90%, respectively, and the sensitivity and specificity were about 90% and 80%. Consequently, the application of Pearson-BPSO feature optimization algorithm will help improve the accuracy of hepatocellular carcinoma detection by eNose and promote the clinical application of intelligent detection.<\/jats:p>","DOI":"10.1155\/2021\/1478384","type":"journal-article","created":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T19:35:07Z","timestamp":1638560107000},"page":"1-9","source":"Crossref","is-referenced-by-count":0,"title":["Feature Optimization of Exhaled Breath Signals Based on Pearson-BPSO"],"prefix":"10.1155","volume":"2021","author":[{"given":"Lijun","family":"Hao","sequence":"first","affiliation":[{"name":"School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"},{"name":"Medical Instrumentation College, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3936-9123","authenticated-orcid":true,"given":"Gang","family":"Huang","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2018.2706"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.3390\/molecules26092447"},{"issue":"7","key":"4","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1002\/hep4.1499","article-title":"Breath metabolomics provides an accurate and noninvasive approach for screening cirrhosis, primary, and secondary liver tumor","volume":"4","author":"M.-A. 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