Intelligent Diagnosis of Asthma Using Machine Learning Algorithms
Journal Title: International Research Journal of Applied and Basic Sciences - Year 2013, Vol 5, Issue 1
Abstract
Data mining in healthcare is a very important field in diagnosis and in deeper understanding of medical data. Health data mining intends to solve real- world problems in diagnosing and treating diseases. One of the most important applications of data mining in the domain of machine learning is diagnosis, and this type of diagnosis of the disease asthma is a notable challenge due to the lack of sufficient knowledge of physicians concerning this disease and because of the complexity of asthma. The purpose of this research is the skillful diagnosis of asthma using efficient algorithms of machine learning. This study was conducted on a data set consisting of 169 asthmatics and 85 non - asthmatics visiting the Imam Khomeini and MasseehDaneshvari Hospitals of Tehran. The algorithms of k – nearest neighbors , random forest , and support vector machine, together with pre – processing and efficient training were implemented on this data set ,and the degrees of accuracy and specificity of the system used in our study were calculated compared with each other and with those of previous research. From among the different values for neighborhood, the highest degree of specificity was achieved with five neighbors. Our method was investigated together with other methods of machine learning and similar research, and the ROC curve was plotted, too. Other methods achieved suitable results as well, and they can be relied on. Therefore, we propose our approach based on the k- nearest algorithm together with pre-processing based on the Relief – F strategy and the Cross Fold data sampling as an efficient method in artificial intelligence with the purpose of data mining for the classification and differential diagnosis of diseases.
Authors and Affiliations
TahaSamadSoltaniHeris| PhD student of medical informatics, the College of Paramedics , Tehran University of Medical Science Tehran , Iran, email:t-ssoltany@razi.tums.ac.ir, MostafaLangarizadeh| assistant professor in medical informatics, the College of Paramedics, Tehran University of Medical Sciences , Tehran , Iran, Zahra Mahmoodvand| M.Sc. student of health information technology, the College of Management and Medical Information , Tehran University of Medical Sciences , Tehran, Iran, Maryam Zolnoori| post – doctoral student in health informatics , the College of Informatics , the State University of Indiana , The United States of America
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