A Clinical Decision Support System for Effective Identification of the Onset of Asthma Disease

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.

Keywords: Correlation, Hybrid, ISAAC, MFCM, Subject clustering.