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Evaluation of Classification Algorithms vs Knowledge-Based Methods for Differential Diagnosis of Asthma in Iranian Patients

Evaluation of Classification Algorithms vs Knowledge-Based Methods for Differential Diagnosis of Asthma in Iranian Patients

Reza Safdari, Peyman Rezaei-Hachesu, Marjan GhaziSaeedi, Taha Samad-Soltani, Maryam Zolnoori
Copyright: © 2018 |Volume: 10 |Issue: 2 |Pages: 14
ISSN: 1935-5688|EISSN: 1935-5696|EISBN13: 9781522543176|DOI: 10.4018/IJISSS.2018040102
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MLA

Safdari, Reza, et al. "Evaluation of Classification Algorithms vs Knowledge-Based Methods for Differential Diagnosis of Asthma in Iranian Patients." IJISSS vol.10, no.2 2018: pp.22-35. http://doi.org/10.4018/IJISSS.2018040102

APA

Safdari, R., Rezaei-Hachesu, P., Marjan GhaziSaeedi, Samad-Soltani, T., & Zolnoori, M. (2018). Evaluation of Classification Algorithms vs Knowledge-Based Methods for Differential Diagnosis of Asthma in Iranian Patients. International Journal of Information Systems in the Service Sector (IJISSS), 10(2), 22-35. http://doi.org/10.4018/IJISSS.2018040102

Chicago

Safdari, Reza, et al. "Evaluation of Classification Algorithms vs Knowledge-Based Methods for Differential Diagnosis of Asthma in Iranian Patients," International Journal of Information Systems in the Service Sector (IJISSS) 10, no.2: 22-35. http://doi.org/10.4018/IJISSS.2018040102

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Abstract

Medical data mining intends to solve real-world problems in the diagnosis and treatment of diseases. This process applies various techniques and algorithms which have different levels of accuracy and precision. The purpose of this article is to apply data mining techniques to the diagnosis of asthma. Sensitivity, specificity and accuracy of K-nearest neighbor, Support Vector Machine, naive Bayes, Artificial Neural Network, classification tree, CN2 algorithms, and related similar studies were evaluated. ROC curves were plotted to show the performance of the authors' approach. Support vector machine (SVM) algorithms achieved the highest accuracy at 98.59% with a sensitivity of 98.59% and a specificity of 98.61% for class 1. Other algorithms had a range of accuracy greater than 87%. The results show that the authors can accurately diagnose asthma approximately 98% of the time based on demographics and clinical data. The study also has a higher sensitivity when compared to expert and knowledge-based systems.

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