Artificial neural networks are able to recognize gastro-oesophageal reflux disease patients solely on the basis of clinical data.
BACKGROUND
Artificial neural networks (ANN) are modelling mechanisms that are highly flexible and adaptive to solve the non-linearity inherent in the relationship between symptoms and underlying pathology.


OBJECTIVE
To assess the efficacy of ANN in achieving a diagnosis of gastro-oesophageal reflux disease (GORD) using oesophagoscopy or pH-metry as a diagnostic gold standard and discriminant analysis as a statistical comparator technique in a group of patients with typical GORD symptoms and with or without GORD objective findings (e.g. a positive oesophagoscopy or a pathological oesophageal pH-metry).


METHODS
The sample of 159 cases (88 men, 71 women) presenting with typical symptoms of GORD, were subdivided on the basis of endoscopy and pH-metry results into two groups: GORD patients with or without oesophagitis, group 1 (N=103), and pH and endoscopy-negative patients in whom both examinations were negative, group 2 (N=56). A total of 101 different independent variables were collected: demographic information, medical history, generic health state and lifestyle, intensity and frequency of typical and atypical symptoms based on the Italian version of the Gastroesophageal Reflux Questionnaire (Mayo Clinic). The diagnosis was used as a dependent variable. Different ANN models were assessed.


RESULTS
Specific evolutionary algorithms selected 45 independent variables, concerning clinical and demographic features, as predictors of the diagnosis. The highest predictive performance was achieved by a 'back propagation' ANN, which was consistently 100% accurate in identifying the correct diagnosis compared with 78% obtained by traditional discriminant analysis.


CONCLUSIONS
On the basis of this preliminary work, the use of ANN seems to be a promising approach for predicting diagnosis without the need for invasive diagnostic methods in patients suffering from GORD symptoms.
Pace
Fabio
F
Department of Gastroenterology, L. Sacco H., Milan, Italy. cn.fapac@tin.it
Buscema
Massimo
M
Dominici
Patrizia
P
Intraligi
Marco
M
Baldi
Fabio
F
Cestari
Renzo
R
Passaretti
Sandro
S
Bianchi Porro
Gabriele
G
Grossi
Enzo
E
D000328:Adult; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004945:Esophagoscopy; D004947:Esophagus / Q000378:metabolism; D005260:Female; D005764:Gastroesophageal Reflux / Q000175:diagnosis*; D006305:Health Status Indicators; D006801:Humans; D006863:Hydrogen-Ion Concentration; D008019:Life Style; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012720:Severity of Illness Index