Predictive Modelling Strategies to Understand Heterogeneous Manifestations of Asthma in Early Life
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Wheezing is common among children and ~50% of those under 6 years of age are thought to experience at least one episode of wheeze. However, due to the heterogeneity of symptoms there are difficulties in treating and diagnosing these children. `Phenotype specific therapy' is one possible avenue of treatment, whereby we use significant pathology and physiology to identify and treat pre-schoolers with wheeze. By performing feature selection algorithms and predictive modelling techniques, this study will attempt to determine if it is possible to robustly distinguish patient diagnostic categories among pre-school children. Univariate feature analysis identified more objective variables and recursive feature elimination a larger number of subjective variables as important in distinguishing between patient categories. Predicative modelling saw a drop in performance when subjective variables were removed from analysis, indicating that these variables are important in distinguishing wheeze classes. We achieved 90%+ performance in AUC, sensitivity, specificity, and accuracy, and 80%+ in kappa statistic, in distinguishing ill from healthy patients. Developed in a synergistic statistical - machine learning approach, our methodologies propose also a novel ROC Cross Evaluation method for model post-processing and evaluation. Our predictive modelling's stability was assessed in computationally intensive Monte Carlo simulations.
Editor(s)
Chen, X
Luo, B
Luo, F
Palade, V
Wani, MA
Date Issued
2018-01-18
Date Acceptance
2017-12-18
Citation
2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2018, pp.68-75
Publisher
IEEE
Start Page
68
End Page
75
Journal / Book Title
2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA)
Copyright Statement
© 2017 IEEE
Sponsor
Medical Research Council (MRC)
Medical Research Council (MRC)
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000425853000011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
MR/M015181/1
MR/K002449/1
Source
16th IEEE International Conference on Machine Learning and Applications (ICMLA)
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Engineering
wheeze
pre-school
feature selection
predictive modelling
ROC analysis
model post-processing
Monte Carlo
1ST 6 YEARS
PRESCHOOL-CHILDREN
DIAGNOSIS
CHILDHOOD
INFANTS
WHEEZE
IMPACT
COUGH
Publication Status
Published
Start Date
2017-12-18
Finish Date
2017-12-21
Coverage Spatial
Cancun, MEXICO