Developing an Intelligent System for Diagnosis of Asthma Based on Artificial Neural Network.
BACKGROUND
Lack of proper diagnosis and inadequate treatment of asthma, leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma.


METHODS
The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique.


RESULTS
According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different modes was made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases.


CONCLUSIONS
Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. So considering the data mining approaches due to the nature of medical data is necessary.
School of Allied-Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Behrouz
B
Alizadeh
Health Information Management Department, School of Allied-Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Reza
R
Safdari
School of Informatics and Computing, Indiana University, Indiana, USA.
Maryam
M
Zolnoori
School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Azadeh
A
Bashiri
Artificial Neural Network; Asthma; Data mining