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Asthma is a common chronic inflammatory disease of the airways that is characterized by aggravated symptoms, reversible airway obstruction, and bronchial spasms (Louis et al., 2000), and the incidence of this chronic disease is estimated to range from 1.4% to 27.1% in different areas of the world (Zolnoori, Zarandi, Moin, Heidarnezhad, & Kazemnejad, 2012b). The prevalence of asthma has increased dramatically since the 1970s, and in 2014 alone, 334 million people were afflicted with asthma worldwide (Network, 2014), and it caused 250,000 deaths (Bousquet et al., 2010). Asthma dramatically affects patients and shows an increasing rate in developing countries (Beasley, Crane, Lai, & Pearce, 2000; Zolnoori, Zarandi, & Moin, 2012). Asthma diminishes the patient’s quality of life. Despite understanding the mechanism of asthma pathology that laboratory facilities have promoted, the treatment process involves several critical problems, including lack of proper diagnosis and incorrect estimation of severity (Zolnoori, Zarandi, & Moin, 2012). Similarly, insufficient experience of general practitioners (GPs) increases the difficulty of producing a correct asthma diagnosis. Physicians, in the diagnosis of asthma, make decisions based on symptoms and patient history (Zarandi, Zolnoori, Moin, & Heidarnejad, 2010).
Data mining is defined as a process for finding patterns and relationships along complex data in a database to build predictive models for decision-making (Kincade, 1998). Moreover, it is the process of selecting, exploring, and building models using mass stored data to identify pre-existing patterns (U. M. Fayyad, Piatetsky-Shapiro, Smyth, & Uthurusamy, 1996; Kavita, 2017). Data mining is used to identify new, accurate, understandable, and potentially useful patterns and relationships within the data by using a combination of sophisticated mining models to apply to human problems (Koh & Tan, 2011; Zhongxian, Ruiliang, Qiyang, & Ruben, 2010). Data mining in health care is used for proper diagnosis and for gaining a deeper understanding of medical data. Medical practitioners use it to solve real-world problems in the diagnosis and treatment of diseases (Liao & Lee, 2002; Soltany, Langarizadeh, & Shanbezadeh, 2013; Samad-Soltanim Ghanei & Langarizadeh,2015). Researchers use it for classification and diagnosis of various diseases; therefore, they apply various techniques and algorithms that possess different levels of accuracy and precision (Shouman, Turner, & Stocker, 2012). Several studies have reported the diagnosis of asthma using data mining algorithms (Alizadeh, Safdari, Zolnoori, & Bashiri, 2015; Chakraborty, Mitra, Mukherjee, & Ray, 2009; Choi et al., 2007; Fazel Zarandi, Zou, Moein, & Heydarnezhad, 2010; Prasadl, Prasad, & Sagar, 2011; Samad Soltani, Langarizadeh, & Zolnoori, 2015; Tyagi & Singh, 2014; Zolnoori, Zarandi, Moin, Heidarnezhad, & Kazemnejad, 2012a).