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Prognostic system for early diagnosis of pediatric lung disease using artificial intelligence.

With the huge growth in the volume of data today, there is an enhanced need to extract meaningful information from the data. Data mining contributes towards this and finds its application across various diverse domains such as in information technology, retail, stock markets, banking and healthcare among others. The surge in population together with the growth in diseases has obliged the insertion of data mining in medical diagnosis to pull out the underlying hidden pattern. Of these, asthma is a disease of high prevalence among children. Asthma prevalence increased from 2001 to 2010 and is now at its highest level. Asthma prevalence was higher among females, children and those with family income below the poverty level. It may be difficult to tell whether your child's symptoms are caused by asthma or something else. Asthma, a disease which is extremely reliant on historical data for early diagnosis, has inclined researchers to follow the data mining techniques for the pre-diagnosis procedure. Existing medical techniques like X-Ray, Spirometer, impulse oscillometry and other lung examination techniques not only require complex equipment and high cost but are also proven to be efficient only when the child is more than 5 years old. The proposed system involves in developing a data mining model that will aid in the grouping of patients into the set that could hypothetically test positive for asthma. Depending on the results obtained as part of pre-diagnosis process from the tool, the doctor can perform the diagnosis for the confirmation of asthma in the patient and initiate the treatment at an early stage.

Author(s): Juliet Rani Rajan, Chilambu Chelvan A