A Hybrid Ensemble Classification Approach to Determine the Impact of Asthma in Association with Gastro Esophageal Reflux Symptoms Kasturi K.1, Prasanna S.2 1Research Scholar, Department of IT, VISTAS 2Associate Professor, Department of Computer Applications, VISTAS Online published on 8 March, 2019. Abstract Objectives This implementation work focuses on the predicting severity of respiratory problems of asthmatic patients from the dataset of the PFT report with the significant parameters of Gastro Esophageal Reflux symptoms (GER). Methodology The pulmonary functionality test (PFT) report of the asthmatic patients is associated with the significant parameters of GER symptoms to determine the impact of GER symptoms on asthma using a proposed hybrid ensemble classification. Methods/Statistical Analysis Using R statistical tool a model has been developed for ensemble classification by stacking the SVM and Random Forest algorithms and boosting with the improved Gradient Boosting algorithm. Findings It has been identified that the asthmatic patients who have been reported as ‘normal’ or ‘mild’ in the PFT report also have the respiratory problems often and urge for frequent check-ups. This can be due to the implications of significant symptom parameters of GER. Applications The outcome of the developed model HMMC describes about the classification accuracy of the applied dataset of the asthmatic patients with GER symptoms and predicts the severity of asthma in asthmatic patients more accurately rather than the outcome of the existing classification techniques. Top Keywords Boosting, asthmatic Patients, PFT, GER, ensemble classification, HMMC. Top |