Design of Personalized Asthma Management System with Data Mining Methods

Cut Fiarni


Asthma is a chronic lung disease that inflames and narrows the airways. Asthma is a multifactorial chronic illness, there is no uniformity of triggers factors of asthma attack on asthma patients with different degree of asthma severity. The better understanding of the trigger factors could lead a better asthma management for asthma patient. in this paper, we proposed a design of personalized asthma management system. in this system we used pattern sequential mining, clustering and classification to predicting the patient's asthma attack based on extracted rules from data mining. the main methodology is to identify the most important trigger factors based on the clustering and classification rules to be extracted. The contribution to this research area is to analyze the suitable data mining technique and algorithm for the proposed system.


Asthma; data mining; personalized management system


Global Initiative for asthma management and prevention, A pocket guide for physicians and nurses, GINA, 2012, pp 5-7

Riset Kesehatan Dasar ,Badan Penelitian dan Pengembangan Kesehatan, Kementrian Kesehatan RI, 2013

Gruffydd-Jones K. Measuring pulmonary function in practice. Practitioner 2002; 246: 445–449. Medline

Global Initiative for Asthma (GINA). Global strategy for asthma management and prevention. Updated 2011. Cape Town: University of Cape Town Lung Institute; 2011

Cios, K.J: Medical data mining and Knowledge Discovery. IEEE engineering in medicine and biology 7. (2000) pp 15-16

Milovic Boris and Milan Milovic, Prediction and Decision Making in Healthcare Using Data Mining. International Journal of Public Health Science (IJPHS) Vol 1, No.2,December 2012,pp.69-78

O'Leary, M. Koolpiruck, D. Balachandran, W. Emberlin, J. Lewis, R. “The role of electrostatic charge accumulated by respirable sized allergens with regard to thunderstorm asthma,” Industry Applications Conference, 2005.

R. L. Jan, J. Y. Wang, M. C. Huang, S. M. Tseng, H. J. Su, L.F. Liu. “An Internet-based interactive telemonitoring system for improving childhood asthma outcomes in Taiwan.” Telemedicine and e-Health, 2006, pp. 1-28.

Dympna O’Sullivan, William Elazmeh, Szymon Wilk, Ken Farion, Stan Matwin, Wojtek Michalowski, and Morvarid Sehatkar, “Using Secondary Knowledge to Support Decision Tree Classification of Retrospective Clinical Data”, MCD 2007, LNAI 4944, pp. 238–251, 200

Tseng Vincent S., Lee Chao-Hui,and Chen Jessie Chia-Yu. “integrated Bio-Signal Data Mining with Applications on Asthma Monitoring”, National Cheng Kung University, Taiwan

Kudyba, S: Managing data mining : advice from experts. (2004)

Zhou Qishen, Z. Yin, Q. Ying, W. Shahnhui, Intelligent Data Mining and Decision System for Commercial Decision Making. TELEKOMNIKA Indonesian Journal of Electrical Engineering Vol.12, No 1, January 2014,pp.792-801

G.Gan,C.Ma, J Wu. “ Data Clustering Theory, Algorithm, and Applications”. American Statistical Association Alexandria, Virginia, 2007

Arun K Punjari, ―Data Mining Techniques‖, Universities (India) Press Private Limited, 2006

P. Bradley, U. Fayyad and C. Reina, "Scaling Clustering Algorithms to Large Databases", In Proc. of the 4th ACM SIGKDD, pp. 9-15, 1998.

Brijesh Kumar Baradwaj, Saurabh Pal, Data mining: machine learning, statistics, and databases, 1996.

Meghana Nagori, Pawar Suvana and Vivek Kshirsagar, “ Managing asthma in children and analyzing best possible treatment with data mining approach of classification”, in Proc of WCECS 2009, San Francisco, USA.

Full Text: PDF


  • There are currently no refbacks.