Comparison of Probabilistic versus Non-probabilistic Electronic Nose Classification Methods in an Animal Model

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.