Asthma Diagnosis Using Neuro-Fuzzy Techniques

Asthma is one of the most common causes of respiratory diseases. By taking into consideration the possibility of this disease worsening over time and its negative impact on patients' daily activities, the continuous monitoring and managing of this disease has become a necessity. In this work, a system is proposed for Asthma diagnosis using adaptive neurofuzzy techniques. The proposed diagnosis system takes key parameters as input including Forced Expiratory Volume (FEVl), Peak Expiratory Flow Rate (PEF) and Forced Vital Capacity (FVC) and predicts Asthma severity condition. A mobile application that allows for easy interface between the patients and the analysis tool is also developed. The proposed system was trained and tested on patient records obtained from two hospitals. The proposed system was able to correctly classify Asthma severity conditions with an average accuracy of 97%.