A data mining algorithm for determination of influential factors on the hospitalization of patients subject to chronic obstructive pulmonary disease
Archives of Medical Laboratory Sciences,
Vol. 4 No. 2 (2018),
13 Mehr 2019
https://doi.org/10.22037/amls.v4i2.25568
Abstract
Background: The present study is on the development of a data mining algorithm for finding the influential factors on the hospitalization of patients subject to chronic obstructive pulmonary disease.
Materials and Methods: This is a descriptive analytical study conducted cross sectionally in 2017 on a research community of 150 people with disease symptoms referred to clinics and hospitals across Tehran (Iran). The people were surveyed by a self-designed questionnaire, including queries on life style and family information. The sampling was simple intuitive from previously published studies. The modeling of the data was based on the CRISP method. The C5 decision tree algorithm was used and the data was analyzed by RapidMiner software.
Results: The common symptoms of the patients were found to be shortness of breath, cough, chest pain, sputum, continuous cold, and cyanogens. Besides, the family history, smoking, and exposure to allergic agents were other influential factors on the disease. After accomplishment of this study, the results were consulted with the experts of the field.
Conclution: It is concluded that data mining can be applied for excavation of knowledge from the gathered data and for determination of the effective factors on patient conditions. Accordingly, this model can successfully predict the disease status of any patient from its symptoms.
- Chronic lung obstruction
- data mining
- decision tree algorithm
- disease status of patient
- RapidMiner
How to Cite
References
Qaseem, A., Wilt, T. J., weinberger, S. E., et al., Diagnosis and management of stable chronic obstructive pulmonary disease: A clinical practice guideline update from the American College of Physicians, American College of Chest Physicians, American Thoracic Society, and European Respiratory Society Annals of Internal Medicine 2011. 155(3): p. 179-191.
Miravitlles, M., Calle, M., Soler-Cataluna, J. J., Clinical phenotypes of COPD: Identification, definition, and implications for guidelines Archivos de Bronconeumologia 2012. 48(3): p. 86-98.
Tan, J., Medical informatics: Concepts, methodologies, tools, and applications. 2009, New York: IGI Global.
Cano, I., Tenyi, A., Schueller, C., Wolff, M., Miguelanez, M. M. H., Gomez-Cabrero, D., Antczak, P., Roca, J., Cascante, M., Falciani, F., Maier, D. , The COPD knowledge base: Enabling data analysis and computational simulation in translational COPD research. Journal of Translational Medicine 2014. 12: p. 2-9.
Cristobalina, R.-A., Felix, R., Enrique, G.-D., Isidro, G.-M., Beatriz, C., Angeles, A., Real-data comparison of data mining methods in early detection of chronic obstructive pulmonary disease (COPD) in general practice Journal of Family Medicine and Disease Prevention 2016. 2(4): p. 1-7.
Sanchez-Morillo, D., Fernandez-Granero, M. A., Leon-Jimenez, A. , Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review Chronic Respiratory Disease 2016. 13(3): p. 264-283.
Al Jarullah, A.A., Decision tree discovery for the diagnosis of type II diabetes in International Conference on Innovations in Information Technology. 2011, IEEE: Abu Dhabi, United Arab Emirates
Alizadeh, S., Ghazanfari, M., Teimorpour, B., Data mining and knowledge discovery. 2011, Tehran, Iran: Publication of Iran University of Science and Technology.
Han, J., Kamber, M., Classification and prediction Data Mining: Concepts and Techniques. 2006, The Netherland Elsevier
Al Jarullah, A.A., Decision tree discovery for the diagnosis of type II diabetes International Conference on Innovations in Information Technology, 2011: p. 303-307.
Zhu, L., Wu, B., Cao, C., Introduction to medical data mining Journal of Biomedical Engineering, 2003. 20(3): p. 559-562.
Yoo, I., Alafaireet, P., Marinov, M., Pena-Hermandez, K., Gopidi, R., Chang, J.-F., Hua, L., Data mining in healthcare and biomedicine: A survey of the literature. Journal of Medical Systems, 2012. 36(4): p. 2431-2448.
Tapak, L., Mahjub, H., Hamidi, O., Poorolajal, J., Real data comparison of data mining methods in prediction of diabetes in Iran. Healthcare Informatics Research, 2013. 19: p. 177-185.
Lee, B.J., Kim, J. Y., A comparison of the predictive power of anthropometric indices for hypertension and hypotension risk. Plos One, 2014. 9: p. e84897.
Sanchez-Morillo, D., Fernandez-Granero, M. A., Leon-Jimenez, A., Use of predictive algorithms in home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review. Chronic Repiratory Disease 2016. 13: p. 264-283.
Dirven, J.A.M., Tange, H. J., Muris, J. W. M., van Haaren, K. M. A., Vink, G., van Schayck, O. C. P. , Early detection of COPD in general practice: patient or practice managed? A randomized controlled trial of two strategies in different socioeconomic environments Primary Care Respiratory Journal 2013. 22: p. 331-337.
Zeng, X., Danquah, M. K., Chen, X. D., Lu, Y., Microalgae bioengineering: From CO2 fixation to biofuel production. Renewable and Sustainable Energy Reviews, 2011. 15: p. 3252-3260.
Price, D.B., Tinkelman, D. G., Halbert, R., Nordyke, R. J., Isonaka, S., Nonikov, D., Juniper, E. F., Freeman, D., Hausen, T., Levy, M. L., Qsterm, A., van der Molen, T., van Schayck, C. P., Symptom based questionnaire for identifying COPD in smokers. Respiration, 2006. 73: p. 285-295.
Burkhardt, R., Pankow, W., The diagnosis of chronic obstructive pulmonary disease Dtsch Arztebl International 2014. 111(49): p. 834-846.
Seyyed Shamsadin Athari, Seyyede Masoume Athari, Fateme Beyzay, Masoud Movassaghi, Esmaeil Mortaz, Mehdi Taghavi. Critical role of Toll-like receptors in pathophysiology of allergic asthma. European Journal of Pharmacology 2017; 808:21–27
Austin, P.C., A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines fr predicting AMI mortality Statistics in Medicine 2006. 26(15): p. 2937-2957.
- Abstract Viewed: 135 times
- PDF Downloaded: 77 times