Comparison of probabilistic versus non-probabilistic electronic nose classification methods in an animal model

Camilla Colombo, Jan Hendrik Leopold, Lieuwe D J Bos, Riccardo Bellazzi, Ameen Abu-Hanna

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An electronic nose (eNose) is a promising device for exhaled breath tests. Principal Component Analysis (PCA) is the most used technique for eNose sensor data analysis, and the use of probabilistic methods is scarce. In this paper, we developed probabilistic models based on the logistic regression framework and compared them to non-probabilistic classification methods in a case study of predicting Acute Liver Failure (ALF) in 16 rats in which ALF was surgically induced. Performance measures included accuracy, AUC and Brier score. Robustness was evaluated by randomly selecting subsets of repeatedly measured sensor values before calculating the model variables. Internal validation for both aspects was obtained by a leave-one-out scheme. The probabilistic methods achieved equally good performance and robustness results when appropriate feature extraction techniques were applied. Since probabilistic models allow employing sound methods for assessing calibration and uncertainty of predictions, they are a proper choice for decision making. Hence we recommend adopting probabilistic classifiers with their associated predictive performance in eNose data analysis.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages298-303
Number of pages6
Volume9105
ISBN (Print)9783319195506
DOIs
Publication statusPublished - 2015
Event15th Conference on Artificial Intelligence in Medicine, AIME 2015 - Pavia, Italy
Duration: Jun 17 2015Jun 20 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9105
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15th Conference on Artificial Intelligence in Medicine, AIME 2015
CountryItaly
CityPavia
Period6/17/156/20/15

Keywords

  • Calibration
  • Discrimination
  • Electronic nose
  • Internal validation
  • Probabilistic classification

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Colombo, C., Leopold, J. H., Bos, L. D. J., Bellazzi, R., & Abu-Hanna, A. (2015). Comparison of probabilistic versus non-probabilistic electronic nose classification methods in an animal model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9105, pp. 298-303). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9105). Springer Verlag. https://doi.org/10.1007/978-3-319-19551-3_38