Abstract
We present a clinical decision support system for the identification of
asthmatics in two different cohorts representing rural and urban populations in India.
The input data representing the two populations are cross-sectional in nature and are
necessarily categorical in nature, with information on clinical history emphasizing
clinical symptoms and patterns characterizing the disease. The system is described as
hybrid as it combines the unsupervised and supervised learning techniques in a unique
way as discussed in the work presented in the paper. The clustering information
emphasizing the phenotypic characterization of asthma is an input to the classifier and
a significant improvement is observed in the performance of the classifier. The results
of the developed hybrid decision support system are quite promising for suitable
deployment in a real-time scenario, as it explores the benefits of both supervised and
unsupervised learning techniques. Further, the use of clustering information in the form
of cluster evaluation scores as an input parameter to the classifiers can efficiently
predict disease outcomes, especially with diseases such as asthma, as the disease is
heterogeneous and exhibits several disease subtypes and heterogeneous phenotypes.