Implications of Asthma over Gastro Esophageal Reflux Symptoms Using Regression Analysis

Today data mining is the major research area for every researcher for extracting useful information from large database. For extracting data they may follow many things like feature reduction, extraction, prediction, etc. This research work mainly concentrates on predicting the data which affects the respiration of asthmatic patients over GER symptoms. For predicting this analysis, initially PFT dataset for asthmatic patients have been collected from the hospital for finding the lung capacity of the patients and further more the GER Symptoms are also evaluated through the collected questionnaire from the asthmatic patients. In this paper the data mining techniques like logistic regression and SVM regression has been applied to the questionnaire data which is collected from asthmatic patients using R statistical tool. Among many parameters from the questionnaire the significant parameters of GER Symptoms that causes the severe respiratory problem to the asthmatic patients has been identified and this implementation work has the accuracy of 97.8%. The found significant parameters of GER symptoms using regression techniques can be used to predict the severity of respiratory problem of asthmatic patients.