Shidiq Nur Hidayat, Trisna Julian, Agus Budi Dharmawan, Mayumi Puspita, Lily Chandra, Abdul Rohman, Madarina Julia, Aditya Rianjanu, Dian Kesumapramudya Nurputra, Kuwat Triyana, Hutomo Suryo Wasisto
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. © 2022 Elsevier B.V.
PT Nanosense Instrument Indonesia, Yogyakarta, Umbulharjo, 55167, Indonesia; Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, BLS 21, Yogyakarta, 55281, Indonesia; Faculty of Information Technology, Universitas Tarumanagara, Jl. Letjen S. Parman No. 1, Jakarta, 11440, Indonesia; RS Bhayangkara Polda Daerah Istimewa Yogyakarta, Jl. Raya Solo-Yogyakarta KM. 14, Sleman, 55571, Indonesia; Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Jl. Farmako Sekip Utara, Yogyakarta, 55281, Indonesia; Department of Child Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Farmako Sekip Utara, Yogyakarta, 55281, Indonesia; Department of Materials Engineering, Institut Teknologi Sumatera, Terusan Ryacudu, Way Hui, Lampung, Jati Agung, 35365, Indonesia
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