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Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.
Hidayat, Shidiq Nur; Julian, Trisna; Dharmawan, Agus Budi; Puspita, Mayumi; Chandra, Lily; Rohman, Abdul; Julia, Madarina; Rianjanu, Aditya; Nurputra, Dian Kesumapramudya; Triyana, Kuwat; Wasisto, Hutomo Suryo.
  • Hidayat SN; PT Nanosense Instrument Indonesia, Umbulharjo, Yogyakarta 55167, Indonesia; Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, BLS 21, Yogyakarta 55281, Indonesia.
  • Julian T; PT Nanosense Instrument Indonesia, Umbulharjo, Yogyakarta 55167, Indonesia.
  • Dharmawan AB; PT Nanosense Instrument Indonesia, Umbulharjo, Yogyakarta 55167, Indonesia; Faculty of Information Technology, Universitas Tarumanagara, Jl. Letjen S. Parman No. 1, Jakarta 11440, Indonesia.
  • Puspita M; PT Nanosense Instrument Indonesia, Umbulharjo, Yogyakarta 55167, Indonesia.
  • Chandra L; RS Bhayangkara Polda Daerah Istimewa Yogyakarta, Jl. Raya Solo-Yogyakarta KM. 14, Sleman 55571, Indonesia.
  • Rohman A; Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Jl. Farmako Sekip Utara, Yogyakarta 55281, Indonesia.
  • Julia M; Department of Child Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Farmako Sekip Utara, Yogyakarta 55281, Indonesia.
  • Rianjanu A; Department of Materials Engineering, Institut Teknologi Sumatera, Terusan Ryacudu, Way Hui, Jati Agung, Lampung 35365, Indonesia.
  • Nurputra DK; Department of Child Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Farmako Sekip Utara, Yogyakarta 55281, Indonesia.
  • Triyana K; Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, BLS 21, Yogyakarta 55281, Indonesia. Electronic address: triyana@ugm.ac.id.
  • Wasisto HS; PT Nanosense Instrument Indonesia, Umbulharjo, Yogyakarta 55167, Indonesia.
Artif Intell Med ; 129: 102323, 2022 07.
Article in English | MEDLINE | ID: covidwho-1906766
ABSTRACT
Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Electronic Nose / COVID-19 Type of study: Diagnostic study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Artif Intell Med Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: J.artmed.2022.102323

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Electronic Nose / COVID-19 Type of study: Diagnostic study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Artif Intell Med Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: J.artmed.2022.102323