Effective asthma disease prediction using naive Bayes — Neural network fusion technique
Saloni Aneja, Sangeeta Lal · 2014
Asthma is a lung disease caused by the inflammation and narrowing of the airways that causes recurrent attacks of breathlessness and wheezing, and often can be life-threatening. Around 15-20 million people are suffering from asthma in India[1]. This paper aims at analyzing various data mining techniques for the prediction of asthma. The observations show that the fusion approach of naive bayes and neural network proved to be the best among classification algorithms in the diagnosis of asthma. This methodology is evaluated using 1024 raw data obtained from a city hospital. The proposed approach helps patients in their diagnosis of asthma.