Advances in Artificial Intelligence / 2013 / Article / Tab 1

Research Article

Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks

Table 1

Prognostic factors.

CategoryPrognostic factors

DemographicAge, sex, ethnicity#, height, weight, waist’s perimeter, residence#
Wheezing episodesUntil 3rd year, between 3rd–5th year, until 5th year
SymptomsWheezing*, cough*, allergic rhinitis*, runny nose*, congestion*, eczema*, food allergy*, pharmaceutical allergy*, allergic conjunctivitis*, dyspnea*, seasonal symptoms#
Parental historyAsthma*
House conditionsNumber of family members, pets*, type of heating#
Pharmaceutical therapyBronchodilators, corticosteroids inhaled*, corticosteroids per os*, antileukotriene*, antihistamine*
Breathing testsFEV1%, FEF25/75%
Tests IgE U/ML
AllergensD. pteronyssinus#, D. farinae#, olive#, pellitory#, graminaceae#, pine#, cypress#, cat#, dog#, alternaria#
Neonatal periodPregnancy duration, breastfeeding duration#, smoking during pregnancy*
AsthmaDiagnosis of asthma*, treatment*

The encoding is binary: yes (1) or no (0).
#The encoding is shown in Table 2.
All other factors are numerical.