Decision Support System for the Diagnosis of Asthma Severity Using Fuzzy Logic
Ashish R Patel, Jyotsna Choubey, Shailendra K. Gupta, Rajendra Prasad, Qamar Rahman · 2012
Asthma is a chronic inflammatory lung disease. Globally Asthma is major public health problem due to its incurable nature and misdiagnosis. In this research paper our work is concerned with the intelligent diagnosis of the severity of the Asthma disease. An automated system has been developed using a self-organizing fuzzy rule-based system. It utilizes the intrinsic ability to deal with the uncertainty and rejects the dealing of add-on mechanisms with imperfect data. Five symptoms have been taken (DSF (Day time symptoms frequency) and NSF (Night time symptoms frequency) PEFR (Peak Expiratory Flow Rate), PEFR variability and SaO2 (Saturation of oxygen) as input and one output for the decision of the asthmatic conditions. For designing of fuzzy inference system rule base play major role in its performance and fine tuning process optimizes the membership functions stored in the data base. The results of the manually constructed inference system was found to be correct when compared with the field data output.