Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spirometry-based airways disease simulation and recognition using
  Machine Learning approaches   The purpose of this study is to provide means to physicians for automated and
fast recognition of airways diseases. In this work, we mainly focus on measures
that can be easily recorded using a spirometer. The signals used in this
framework are simulated using the linear bi-compartment model of the lungs.
This allows us to simulate ventilation under the hypothesis of ventilation at
rest (tidal breathing). By changing the resistive and elastic parameters, data
samples are realized simulating healthy, fibrosis and asthma breathing. On this
synthetic data, different machine learning models are tested and their
performance is assessed. All but the Naive bias classifier show accuracy of at
least 99%. This represents a proof of concept that Machine Learning can
accurately differentiate diseases based on manufactured spirometry data. This
paves the way for further developments on the topic, notably testing the model
on real data.

18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat