{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T19:35:27Z","timestamp":1782243327902,"version":"3.54.5"},"reference-count":81,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T00:00:00Z","timestamp":1623974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004442","name":"Narodowym Centrum Nauki","doi-asserted-by":"publisher","award":["2017\/26\/D\/ST7\/00355"],"award-info":[{"award-number":["2017\/26\/D\/ST7\/00355"]}],"id":[{"id":"10.13039\/501100004442","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Exhaled breath analysis has become more and more popular as a supplementary tool for medical diagnosis. However, the number of variables that have to be taken into account forces researchers to develop novel algorithms for proper data interpretation. This paper presents a system for analyzing exhaled air with the use of various sensors. Breath simulations with acetone as a diabetes biomarker were performed using the proposed e-nose system. The XGBoost algorithm for diabetes detection based on artificial breath analysis is presented. The results have shown that the designed system based on the XGBoost algorithm is highly selective for acetone, even at low concentrations. Moreover, in comparison with other commonly used algorithms, it was shown that XGBoost exhibits the highest performance and recall.<\/jats:p>","DOI":"10.3390\/s21124187","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T11:19:20Z","timestamp":1624015160000},"page":"4187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1467-3017","authenticated-orcid":false,"given":"Anna","family":"Paleczek","sequence":"first","affiliation":[{"name":"Institute of Electronics, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9970-710X","authenticated-orcid":false,"given":"Dominik","family":"Grochala","sequence":"additional","affiliation":[{"name":"Institute of Electronics, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9148-1094","authenticated-orcid":false,"given":"Artur","family":"Rydosz","sequence":"additional","affiliation":[{"name":"Institute of Electronics, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1038\/scientificamerican0792-74","article-title":"Breath tests in medicine","volume":"267","author":"Phillips","year":"1992","journal-title":"Sci. Am."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Selvaraj, R., Vasa, N.J., Nagendra, S.M.S., and Mizaikoff, B. (2020). Advances in Mid-Infrared Spectroscopy-Based Sensing Techniques for Exhaled Breath Diagnostics. Molecules, 25.","DOI":"10.3390\/molecules25092227"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"032001","DOI":"10.1088\/1752-7163\/ab1789","article-title":"Targeted breath analysis: Exogenous volatile organic compounds (EVOC) as metabolic pathway-specific probes","volume":"13","author":"Gaude","year":"2019","journal-title":"J. Breath Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Longo, V., Forleo, A., Ferramosca, A., Notari, T., Pappalardo, S., Siciliano, P., Capone, S., and Montano, L. (2021). Blood, urine and semen Volatile Organic Compound (VOC) pattern analysis for assessing health environmental impact in highly polluted areas in Italy. Environ. Pollut., 117410.","DOI":"10.1016\/j.envpol.2021.117410"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e4132","DOI":"10.1002\/bmc.4132","article-title":"Chromatographic analysis of VOC patterns in exhaled breath from smokers and nonsmokers","volume":"32","author":"Capone","year":"2018","journal-title":"Biomed. Chromatogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1177\/1535370217750088","article-title":"Biomarker definitions and their applications","volume":"243","author":"Califf","year":"2018","journal-title":"Exp. Biol. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.anai.2011.02.016","article-title":"Human exhaled breath analysis","volume":"106","author":"Popov","year":"2011","journal-title":"Ann. Allergy Asthma Immunol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1590\/S1806-37132010000600005","article-title":"Exhaled breath temperature, a new biomarker in asthma control: A pilot study","volume":"36","author":"Melo","year":"2010","journal-title":"J. Bras. Pneumol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1081\/JAS-120033990","article-title":"Exhaled nitric oxide predicts asthma exacerbation","volume":"41","author":"Harkins","year":"2004","journal-title":"J. Asthma"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sakumura, Y., Koyama, Y., Tokutake, H., Hida, T., Sato, K., Itoh, T., Akamatsu, T., and Shin, W. (2017). Diagnosis by volatile organic compounds in exhaled breath from lung cancer patients using support vector machine algorithm. Sensors, 17.","DOI":"10.3390\/s17020287"},{"key":"ref_11","first-page":"S540","article-title":"Exhaled breath analysis for lung cancer","volume":"5","author":"Dent","year":"2013","journal-title":"J. Thorac. Dis."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.compbiomed.2018.04.002","article-title":"Early non-invasive detection of breast cancer using exhaled breath and urine analysis","volume":"96","author":"Boger","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.critrevonc.2012.11.007","article-title":"Diagnosis of breast cancer based on breath analysis: An emerging method","volume":"87","author":"Li","year":"2013","journal-title":"Crit. Rev. Oncol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"034002","DOI":"10.1088\/1752-7155\/10\/3\/034002","article-title":"A systematic review of breath analysis and detection of volatile organic compounds in COPD","volume":"10","author":"Christiansen","year":"2016","journal-title":"J. Breath Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.clinms.2018.02.003","article-title":"Real-time mass spectrometric identification of metabolites characteristic of chronic obstructive pulmonary disease in exhaled breath","volume":"7","author":"Bregy","year":"2018","journal-title":"Clin. Mass Spectrom."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"037109","DOI":"10.1088\/1752-7155\/7\/3\/037109","article-title":"Is breath acetone a biomarker of diabetes? A historical review on breath acetone measurements","volume":"7","author":"Wang","year":"2013","journal-title":"J. Breath Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.diabres.2012.02.006","article-title":"The clinical potential of exhaled breath analysis for diabetes mellitus","volume":"97","author":"Minh","year":"2012","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/S1570-0232(04)00657-9","article-title":"Determination of acetone in human breath by gas chromatography\u2013mass spectrometry and solid-phase microextraction with on-fiber derivatization","volume":"810","author":"Deng","year":"2004","journal-title":"J. Chromatogr. B"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1203\/00006450-199809000-00016","article-title":"Exhaled isoprene and acetone in newborn infants and in children with diabetes mellitus","volume":"44","author":"Nelson","year":"1998","journal-title":"Pediatr. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"011001","DOI":"10.1088\/1752-7155\/1\/1\/011001","article-title":"Acetone, ammonia and hydrogen cyanide in exhaled breath of several volunteers aged 4\u201383 years","volume":"1","author":"Dryahina","year":"2007","journal-title":"J. Breath Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"017115","DOI":"10.1088\/1752-7155\/7\/1\/017115","article-title":"Monitoring breath during oral glucose tolerance tests","volume":"7","author":"Ghimenti","year":"2013","journal-title":"J. Breath Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.1016\/j.jchromb.2009.06.039","article-title":"Breath acetone analysis with miniaturized sample preparation device: In-needle preconcentration and subsequent determination by gas chromatography\u2013mass spectroscopy","volume":"877","author":"Ueta","year":"2009","journal-title":"J. Chromatogr. B"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rydosz, A. (2018). Sensors for enhanced detection of acetone as a potential tool for noninvasive diabetes monitoring. Sensors, 18.","DOI":"10.3390\/s18072298"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1007\/s00216-014-8401-8","article-title":"Determination of breath acetone in 149 Type 2 diabetic patients using a ringdown breath-acetone analyzer","volume":"407","author":"Sun","year":"2015","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/JSEN.2009.2035675","article-title":"The future of sensors and instrumentation for human breath analysis","volume":"10","author":"Davis","year":"2010","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1002\/bmc.835","article-title":"Human exhaled air analytics: Biomarkers of diseases","volume":"21","author":"Buszewski","year":"2007","journal-title":"Biomed. Chromatogr."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"044001","DOI":"10.1088\/1752-7155\/7\/4\/044001","article-title":"Hydrogen cyanide, a volatile biomarker of Pseudomonas aeruginosa infection","volume":"7","author":"Smith","year":"2013","journal-title":"J. Breath Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"036004","DOI":"10.1088\/1752-7155\/6\/3\/036004","article-title":"An investigation of suitable bag materials for the collection and storage of breath samples containing hydrogen cyanide","volume":"6","author":"Gilchrist","year":"2012","journal-title":"J. Breath Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.chroma.2013.05.012","article-title":"Detection of potential chronic kidney disease markers in breath using gas chromatography with mass-spectral detection coupled with thermal desorption method","volume":"1301","author":"Faber","year":"2013","journal-title":"J. Chromatogr. A"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/S0140-6736(77)91605-1","article-title":"Breath-methane in patients with cancer of the large bowel","volume":"310","author":"Haines","year":"1977","journal-title":"Lancet"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3109\/00365529209011161","article-title":"Breath methane and colorectal cancer","volume":"27","author":"Sivertsen","year":"1992","journal-title":"Scand. J. Gastroenterol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1016\/0140-6736(91)91569-G","article-title":"High breath pentane concentrations during acute myocardial infarction","volume":"337","author":"Weitz","year":"1991","journal-title":"Lancet"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"117","DOI":"10.3109\/10715769509064026","article-title":"Expired hydrocarbons in patients with acute myocardial infarction","volume":"23","author":"Mendis","year":"1995","journal-title":"Free Radic. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.1378\/chest.111.6.1500","article-title":"Exhaled pentane and nitric oxide levels in patients with obstructive sleep apnea","volume":"111","author":"Olopade","year":"1997","journal-title":"Chest"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1038\/ki.1997.324","article-title":"Quantitative analysis of ammonia on the breath of patients in end-stage renal failure","volume":"52","author":"Davies","year":"1997","journal-title":"Kidney Int."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s00340-011-4716-8","article-title":"Ethylene and ammonia traces measurements from the patients\u2019 breath with renal failure via LPAS method","volume":"105","author":"Popa","year":"2011","journal-title":"Appl. Phys. B"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.snb.2017.10.178","article-title":"Exhaled breath analysis using electronic nose and gas chromatography\u2013mass spectrometry for non-invasive diagnosis of chronic kidney disease, diabetes mellitus and healthy subjects","volume":"257","author":"Saidi","year":"2018","journal-title":"Sens. Actuators B Chem."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"026007","DOI":"10.1088\/1752-7163\/aa6ac6","article-title":"Analyzing breath samples of hypoglycemic events in type 1 diabetes patients: Towards developing an alternative to diabetes alert dogs","volume":"11","author":"Siegel","year":"2017","journal-title":"J. Breath Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"046011","DOI":"10.1088\/1752-7155\/5\/4\/046011","article-title":"Measurement of breath acetone concentrations by selected ion flow tube mass spectrometry in type 2 diabetes","volume":"5","author":"Storer","year":"2011","journal-title":"J. Breath Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"046001","DOI":"10.1088\/1752-7155\/4\/4\/046001","article-title":"Accurate, reproducible measurement of acetone concentration in breath using selected ion flow tube-mass spectrometry","volume":"4","author":"Dummer","year":"2010","journal-title":"J. Breath Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"027007","DOI":"10.1088\/1752-7155\/3\/2\/027007","article-title":"Investigations on the variability of breath gas sampling using PTR-MS","volume":"3","author":"Thekedar","year":"2009","journal-title":"J. Breath Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1109\/JSEN.2013.2245888","article-title":"A micropreconcentrator design using low temperature cofired ceramics technology for acetone detection applications","volume":"13","author":"Rydosz","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"921","DOI":"10.3390\/metabo4040921","article-title":"Micropreconcentrator in LTCC technology with mass spectrometry for the detection of acetone in healthy and type-1 diabetes mellitus patient breath","volume":"4","author":"Rydosz","year":"2014","journal-title":"Metabolites"},{"key":"ref_44","unstructured":"WHO (2019). Global Report on Diabetes (2019), WHO."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1016\/S0140-6736(14)60613-9","article-title":"Prevention and management of type 2 diabetes: Dietary components and nutritional strategies","volume":"383","author":"Ley","year":"2014","journal-title":"Lancet"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100254","DOI":"10.1016\/j.imu.2019.100254","article-title":"Development of non-invasive diabetes risk prediction models as decision support tools designed for application in the dental clinical environment","volume":"17","author":"Hegde","year":"2019","journal-title":"Inform. Med. Unlocked"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jiang, C., Sun, M., Wang, Z., Chen, Z., Zhao, X., Yuan, Y., Li, Y., and Wang, C. (2016). A portable real-time ringdown breath acetone analyzer: Toward potential diabetic screening and management. Sensors, 16.","DOI":"10.3390\/s16081199"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Saasa, V., Beukes, M., Lemmer, Y., and Mwakikunga, B. (2019). Blood ketone bodies and breath acetone analysis and their correlations in type 2 diabetes mellitus. Diagnostics, 9.","DOI":"10.3390\/diagnostics9040224"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/JSEN.2009.2035730","article-title":"A study on breath acetone in diabetic patients using a cavity ringdown breath analyzer: Exploring correlations of breath acetone with blood glucose and glycohemoglobin A1C","volume":"10","author":"Wang","year":"2009","journal-title":"IEEE Sens. J."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"027003","DOI":"10.1088\/1752-7155\/3\/2\/027003","article-title":"Breath acetone\u2014aspects of normal physiology related to age and gender as determined in a PTR-MS study","volume":"3","author":"Schwarz","year":"2009","journal-title":"J. Breath Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.aca.2004.12.018","article-title":"Determination of acetone in breath","volume":"535","author":"Teshima","year":"2005","journal-title":"Anal. Chim. Acta"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"127371","DOI":"10.1016\/j.snb.2019.127371","article-title":"Measurement of temperature and relative humidity in exhaled breath","volume":"304","author":"Mansour","year":"2020","journal-title":"Sens. Actuators B Chem."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/0034-5687(80)90067-5","article-title":"Respiratory water loss","volume":"39","author":"Ferrus","year":"1980","journal-title":"Respir. Physiol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"046001","DOI":"10.1088\/1752-7155\/2\/4\/046001","article-title":"On the use of Tedlar\u00ae bags for breath-gas sampling and analysis","volume":"2","author":"Beauchamp","year":"2008","journal-title":"J. Breath Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"315502","DOI":"10.1088\/0957-4484\/20\/31\/315502","article-title":"Minimal cross-sensitivity to humidity during ethanol detection by SnO2\u2013TiO2 solid solutions","volume":"20","author":"Tricoli","year":"2009","journal-title":"Nanotechnology"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.1109\/JBHI.2017.2757510","article-title":"Real-time non-invasive detection and classification of diabetes using modified convolution neural network","volume":"22","author":"Lekha","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.1109\/TBME.2010.2055864","article-title":"A novel breath analysis system based on electronic olfaction","volume":"57","author":"Guo","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yan, K., and Zhang, D. (2012, January 17\u201318). A novel breath analysis system for diabetes diagnosis. Proceedings of the 2012 International Conference on Computerized Healthcare (ICCH), Hong Kong, China.","DOI":"10.1109\/ICCH.2012.6724490"},{"key":"ref_59","unstructured":"Sarno, R., Sabilla, S.I., and Wijaya, D.R. (2020). Electronic Nose for Detecting Multilevel Diabetes using Optimized Deep Neural Network. Eng. Lett., 28."},{"key":"ref_60","unstructured":"Sarno, R., and Wijaya, D.R. (2017, January 31). Detection of diabetes from gas analysis of human breath using e-Nose. Proceedings of the 2017 11th International Conference on Information & Communication Technology and System (ICTS), Surabaya, Indonesia."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1109\/TBME.2014.2329753","article-title":"Design of a breath analysis system for diabetes screening and blood glucose level prediction","volume":"61","author":"Yan","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Guo, D., Zhang, D., Li, N., Zhang, L., and Yang, J. (2010). Diabetes identification and classification by means of a breath analysis system. Proceedings of the International Conference on Medical Biometrics, Hong Kong, China, 28\u201330 June 2010, Springer.","DOI":"10.1007\/978-3-642-13923-9_6"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Lekha, S., and Suchetha, M. (2015, January 2\u20134). Non-invasive diabetes detection and classification using breath analysis. Proceedings of the 2015 International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, India.","DOI":"10.1109\/ICCSP.2015.7322639"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"016005","DOI":"10.1088\/1752-7163\/abc09b","article-title":"Adsorption kinetics feature extraction from breathprint obtained by graphene based sensors for diabetes diagnosis","volume":"15","author":"Kalidoss","year":"2020","journal-title":"J. Breath Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1109\/JSEN.2017.2780178","article-title":"A novel 1-D convolution neural network with SVM architecture for real-time detection applications","volume":"18","author":"Lekha","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhang, D., Guo, D., and Yan, K. (2017). Breath Signal Analysis for Diabetics. Breath Analysis for Medical Applications, Springer.","DOI":"10.1007\/978-981-10-4322-2"},{"key":"ref_67","unstructured":"Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., and Grobler, J. (2013). API design for machine learning software: Experiences from the scikit-learn project. ECML PKDD Workshop: Languages for Data Mining and Machine Learning, Springer."},{"key":"ref_68","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13\u201317 August 2016, ACM. KDD \u201916.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.talanta.2013.12.025","article-title":"Applications of Hadamard transform-gas chromatography\/mass spectrometry to the detection of acetone in healthy human and diabetes mellitus patient breath","volume":"120","author":"Fan","year":"2014","journal-title":"Talanta"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2407-9-348","article-title":"Noninvasive detection of lung cancer by analysis of exhaled breath","volume":"9","author":"Bajtarevic","year":"2009","journal-title":"BMC Cancer"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neucom.2013.05.059","article-title":"A method for resampling imbalanced datasets in binary classification tasks for real-world problems","volume":"135","author":"Cateni","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Liu, W., and Chawla, S. (2011). Class confidence weighted knn algorithms for imbalanced data sets. Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer.","DOI":"10.1007\/978-3-642-20847-8_29"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Akbani, R., Kwek, S., and Japkowicz, N. (2004). Applying support vector machines to imbalanced datasets. European Conference on Machine Learning, Springer.","DOI":"10.1007\/978-3-540-30115-8_7"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s40708-017-0065-7","article-title":"Machine learning\u2013XGBoost analysis of language networks to classify patients with epilepsy","volume":"4","author":"Torlay","year":"2017","journal-title":"Brain Inform."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.1109\/TCBB.2019.2911071","article-title":"XGBoost Model for Chronic Kidney Disease Diagnosis","volume":"17","author":"Ogunleye","year":"2020","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Ogunleye, A., and Wang, Q.G. (2018, January 12\u201315). Enhanced XGBoost-based automatic diagnosis system for chronic kidney disease. Proceedings of the 2018 IEEE 14th International Conference on Control and Automation (ICCA), Anchorage, AK, USA.","DOI":"10.1109\/ICCA.2018.8444167"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_79","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_80","unstructured":"G\u00e9ron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis. ROC Analysis in Pattern Recognition","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/12\/4187\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:18:19Z","timestamp":1760163499000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/12\/4187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,18]]},"references-count":81,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["s21124187"],"URL":"https:\/\/doi.org\/10.3390\/s21124187","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,18]]}}}