Abstract

Background. - Data mining is a technique widely used in medicine for the medical diagnosis, treatment and prognosis. Amongst the medical applications of data mining techniques, the most important are the construction of models comprising the key factors in disease diagnosis, evaluation of disease-related data patterns, monitoring of patient health status, and forecasting patients' future status. The purpose of this study was to assess key factors in the onset/progression of asthma central to accurate diagnosis of the disease. Methods. - The survey was designed as a cross-sectional descriptive study in the summer of 2017. We selected patients diagnosed with asthma (n = 300) and individuals without asthma (n = 80) referred to clinics and hospitals in Tehran (Iran), Mofid Hospital (Tehran) and the Children's Medical Center (Tehran). A questionnaire was used to collect complementary medical information from the selected individuals. The recorded information was then evaluated and 200 of these cases were selected for further analysis. The surveyed data was analyzed using MATLAB and RapidMiner software and the results were assessed using a decision-tree algorithm. Results and Discussion. - The key disease factors and their interrelation were analysed in the present study. Shortness of breath, wheezing, repeated respiratory attacks, cough, sleep disorder and facial bruising are common symptoms of asthma from the mildest to the most severe forms. Family history was also found to be an important factor in asthma risk for children. The findings of the study are consistent with those of expert physicians, with confirmation of the ability of data mining to generate knowledge from experimental data and to establish a consensual diagnosis. (C) 2019 Elsevier Masson SAS. All rights reserved.