Health Consumer Usage Patterns in Management of CVD using Data Mining Techniques
Article No.: 28, Pages 1 - 9
Abstract
The Healthcare system is exposed to the increasing impact of chronic diseases including cardiovascular diseases; it is of much importance to analyze and understand the health trajectories for efficient planning and fair allotment of resources. This work proposes an approach based on mining clinical data to support the exploration of health trajectories related to cardiovascular diseases. As the health data are highly confidential, we aimed to conduct our experiments using a large, synthetic, longitudinal dataset, constituted to represent the CVD risk factors distribution and temporal sequence of events related to heart failure hospitalization and readmission.
This research work analyses and represents the temporal events or states of the patient's trajectory with the aim of understanding the patient's journey in the management of the chronic condition and its complications by using data mining techniques. This study focuses on developing an efficient algorithm to find cohesive clusters for handling the temporal events. Clustering health trajectories have been carried out by proposing an improved version of the Ant-based clustering algorithm. Insights from this study can potentially result in evidence that these approaches are useful in understanding and analyzing patient's health trajectories for better management of the chronic condition and its progression.
References
[1]
Song, S., Warren, J., & Riddle, P. (2014, May). Profiling Cardiovascular Disease Event Risk through Clustering of Classification Association Rules. In Computer-Based Medical Systems (CBMS), 2014 IEEE 27th International Symposium on (pp. 294--299). IEEE.
[2]
Jay, N., Nuemi, G., Gadreau, M., & Quantin, C. (2013). A data mining approach for grouping and analyzing trajectories of care using claim data: The example of breast cancer. BMC Medical Informatics and Decision Making, 13(1).
[3]
Ministry of Health (2015) Mortality and Demographic data 2013 provisional. Wellington: Ministry of Health
[4]
Liao, M., Li, Y., Kianifard, F., Obi, E., & Arcona, S. (2016). Cluster analysis and its application to healthcare claims data: A study of end-stage renal disease patients who initiated hemodialysis. BMC Nephrology, 17(1).
[5]
Statistics NewZealand. (2013) Census data user guide. Wellington, New Zealand statistics., Newzealand.
[6]
Dilts, D., Khamalah, J., & Plotkin, A. (1995). Using cluster analysis for medical resource decision making. Medical Decision Making, 15(4), 333--346.
[7]
Bosomworth, N. J. (2011). Practical use of the Framingham risk score in primary prevention: a Canadian perspective. Canadian Family Physician, 57(4), 417--423.
[8]
Nordhausen, K. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman. International Statistical Review, 77(3), 482--482.
[9]
Handl, J., Knowles, J., & Dorigo, M. (2006). Ant-based clustering and topographic mapping. Artificial life, 12(1), 35--62.
[10]
Bonabeau E, Dorigo M, Theraulaz G. (1999). Swarm intelligence, From natural to artificial systems. New York, Oxford University Press.
[11]
Deneubourg, J.-L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., & Chrétien, L. (1991). The dynamics of collective sorting: Robot-like ants and antlike robots. In Proceedings of the First International Conference on Simulation of Adaptive Behavior: From animals to animats 1 (pp. 356--365). Cambridge, MA: MIT Press.
[12]
Dorigo, M., Bonabeau, E., Theraulaz, G. (2000). Ant algorithms and stigmergy. Future Generation Computer Systems, 16(8), 851--871.
[13]
Engelbrecht, A. P. (2006). Fundamentals of computational swarm intelligence. John Wiley & Sons.
[14]
Martens, D., Baesens, B., & Fawcett, T. (2011). Editorial survey: swarm intelligence for data mining. Machine Learning, 82(1), 1--42.
[15]
Zaharie, D., & Zamfirache, F. (2005, September). Dealing with noise in ant-based clustering. In Evolutionary Computation, 2005. The 2005 IEEE Congress on (Vol. 3, pp. 2395--2401). IEEE.
[16]
Boryczka, U. (2008). Ant clustering algorithm. Intelligent Information Systems, 1998, 455--458.
[17]
Weili, Z. (2009, July). An improved entropy-based ant clustering algorithm. In 2009 WASE International Conference on Information Engineering (pp. 41--44). IEEE.
[18]
Hameurlaine, M., Moussaoui, A., & Cherroun, H. (2012).Ant means: A new hybrid algorithm based on ant colonies for complex data mining. International Journal of Computer Applications (0975-8887) Volume.
[19]
Gu, Y., & Hall, L. O. (2006). Kernel-based fuzzy ant clustering with partition validity. In Proceedings of the IEEE international conference on fuzzy systems (pp. 263--267). Piscataway: IEEE Press.
[20]
Kanade, P. M., & Hall, L. O. (2003). Fuzzy ants as a clustering concept. In NAFIPS 2003: 22nd international conference of the North American fuzzy information processing society (pp. 227--232). Piscataway: IEEE Press.
[21]
Kanade, P. M., & Hall, L. O. (2004). Fuzzy ant clustering by centroid positioning. In Proceedings of the IEEE international conference on fuzzy systems (Vol. 1, pp. 371--376). Piscataway: IEEE Press.
[22]
Monmarché, N., Slimane, M., & Venturini, G. (1999). On improving clustering in numerical databases with artificial ants. In D. Floreano, J.-D. Nicoud, & F. Mondada (Eds.), Lecture notes in artificial intelligence: Vol. 1674. Advances in artificial life: 5th European conference, ECAL 99 (pp. 626--635). Berlin: Springer.
[23]
Monmarché, N., Ramat, E., Desbarats, L., & Venturini, G. (2000). Probabilistic search with genetic algorithms and ant colonies. In A. S. Wu (Ed.), Workshop on optimization by building and using probabilistic models, GECCO 2000 (pp. 209--211).
[24]
Li, Q., Shi, Z., Shi, J., & Shi, Z. (2005). Swarm intelligence clustering algorithm based on attractor. In L. Wang, K. Chen, & Y.-S. Ong (Eds.), Lecture notes in computer science: Vol. 3612. Advances in natural computation, first international conference, ICNC 2005 (pp. 496--504). Berlin: Springer.
[25]
Lumer, E., & Faieta, B. (1994). Diversity and adaptation in populations of clustering ants. In Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3 (pp. 501--508). Cambridge, MA: MIT Press.
[26]
Ramos, V., & Merelo, J. (2002). Self-organized stigmergic document maps: environments as a mechanism for context learning. In Proceedings of the first Spanish conference on evolutionary and bio-inspired algorithms (pp. 284--293). Mérida: Centro Univ. Mérida
[27]
Vizine, A. L., de Castro, L. N., Hruschka, E. R., & Gudwin, R. R. (2005b). Towards improving clustering ants: an adaptive ant clustering algorithm. Informatica, 29, 143--154.
[28]
Montes de Oca, M. A., Garrido, L., & Aguirre, J. L. (2005). Effects of inter-agent communication in Ant-based clustering algorithms: a case study on communication policies in swarm systems. In A. Gelbukh & H. Terashima (Eds.), Lecture notes in artificial intelligence: Vol. 3789. MICAI 2005: advances in artificial intelligence: 4th Mexican international conference on artificial intelligence (pp. 254--263). Berlin: Springer.
[29]
Schockaert, S., Cock, M. D., Cornelis, C., & Kerre, E. E. (2004a). Efficient clustering with fuzzy ants. In D. Ruan, P. D'hondt, M. D. Cock, M. Nachtegael, & E. E. Kerre (Eds.), Applied computational intelligence, proceedings of the 6th international FLINS conference (pp. 195--200). River Edge: World Scientific.
[30]
Schockaert, S., Cock, M. D., Cornelis, C., & Kerre, E. E. (2004b). Fuzzy Ant-based clustering. In M. Dorigo, M. Birattari, C. Blum, L. M. Gambardella, F. Mondada, & T. Stützle (Eds.), Lecture notes in computer science: Vol. 3172. Ant colony optimization and swarm intelligence, 4th international workshop, ANTS 2004 (pp. 342--349). Berlin: Springer
[31]
Das, Gunopulos, Mannila(1997). Finding Similar Time Series. In: Proceeding of the First PKDD Symptoms.
[32]
Zhang, Y., Padman, R., & Wasserman (2014). On Learning and Visualizing Practice-based Clinical Pathways for Chronic Kidney Disease. AMIA Annual Symposium Proceedings.
[33]
Namiki, Y., Ishida, T., Akiyama, Y. (2013). Acceleration of sequence clustering using longest common subsequence filtering. BMC Bioinformatics, 14(Suppl 8), S7.
[34]
Chen, Y., Lu, H., & Li, L. (2017). Automatic ICD-10 coding algorithm using an improved longest common subsequence based on semantic similarity. PLoS ONE, 12(3), e0173410.
[35]
Park, K., Lin, Y., Metsis, V., Le, Z., & Makedon, F. (2010). Abnormal human behavioral pattern detection in assisted living environments. In Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environments (p. 9). ACM.
[36]
Bannink, L., Wells, S., Broad, J., Riddell, T., & Jackson, R. (2006, November 17). Web-based assessment of cardiovascular disease risk in routine primary care practice in New Zealand: The first 18,000 patients (PREDICT CVD-1). Retrieved from https://www.ncbi.nlm.nih.gov/ /17146488.
[37]
P.W, Wilson D, R.B, Levy, Belanger, Silbershatz, Kannel, W.B. (1998). Prediction of coronary heart disease using risk factor categories. 97 (18): 1837--1847. PMID 9603539.
[38]
Reddy, R. K. Y., Mahendra, J., & Gurumurthy, P. (2015). Identification of predictable biomarkers in conjunction to Framingham risk score to predict the risk for cardiovascular disease (CVD) in Non cardiac subjects. Journal of clinical and diagnostic research: JCDR, 9(2), BC23.
[39]
Anderson KM, Odell PM, Wilson PWF, Kannel WB (1991). Cardiovascular disease risk profiles. American Heart Journal,121(1, Part 2):293.
[40]
Lloyd-Jones, D. M., Wilson, P. W., Larson, M. G., Beiser, A., Leip, E. P., D'Agostino, R. B., & Levy, D. (2004). Framingham risk score and prediction of lifetime risk for coronary heart disease. The American journal of cardiology, 94(1), 20--24.
[41]
Tsipouras, Karvounis EC, Tzallas AT, Katertsidis NS, Goletsis Y, Frigerio M, Verde A, Trivella MG, Fotiadis DI (2013). Adverse event prediction in patients with left ventricular assist devices. In: Proceedings IEEE Medical Biological Society.
[42]
New Zealand Guidelines Group(2003). Assessment and Management of Cardiovascular Risk. Wellington. https://www.health.govt.nz/system/files/documents/publications/cvd_risk_summary.pdf
[43]
Chaoji, V., Al Hasan, M., Salem, S., & Zaki, M. J. (2008, December). Sparcl: Efficient and effective shape-based clustering. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on (pp. 93--102). IEEE.
[44]
Das, Gunopulos, Mannila(1997). Finding Similar Time Series. In: Proceeding of the First PKDD Symptoms.
[45]
Knight, J., Wells, S., Marshall, R., Exeter, D., Jackson, R. (2017). Developing a synthetic national population to investigate the impact of different cardiovascular disease risk management strategies: A derivation and validation study. PLOS ONE, 12(4), p. e0173170.
[46]
Rousseeuw J, Silhouettes P (1987). A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, Vol. 20, pp. 53--65.
[47]
Davies D, Bouldin D (1979). A cluster separation measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2, pp. 224--227.
[48]
Dunn J (1974). Well-separated clusters and optimal fuzzy partitions, Journal of Cybernetics, Vol.4, pp. 95--104.
Index Terms
- Health Consumer Usage Patterns in Management of CVD using Data Mining Techniques
Recommendations
Analysis of treatment pathways for three chronic diseases using OMOP CDM
AbstractThe present study examined treatment pathways (the ordered sequence of medications that a patient is prescribed) for three chronic diseases (hypertension, type 2 diabetes, and depression), compared the pathways with recommendations from guidelines,...
PHR Based Diabetes Index Service Model Using Life Behavior Analysis
Due to our rapidly aging society, there has been an increasing need for preventive management of chronic diseases and management of individual health conditions. Among the chronic diseases, diabetes has become one of the most important illnesses in the ...
Using Decision Tree to Analyze Patient of Aortic Aneurysm with Chronic Diseases in Clinical Application
NBIS '13: Proceedings of the 2013 16th International Conference on Network-Based Information SystemsAortic aneurysm is caused by sclerosis of aortic wall. However, bad habits and chronic disease would be caused sclerosis of aortic wall. Therefore, bad habits and chronic diseases would possible affect aortic aneurysm, even after surgery. In this study, ...
Comments
Information & Contributors
Information
Published In
January 2019
486 pages
ISBN:9781450366038
DOI:10.1145/3290688
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- CORE - Computing Research and Education
- Macquarie University-Sydney
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 January 2019
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ACSW 2019
Acceptance Rates
ACSW '19 Paper Acceptance Rate 61 of 141 submissions, 43%;
Overall Acceptance Rate 61 of 141 submissions, 43%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 87Total Downloads
- Downloads (Last 12 months)7
- Downloads (Last 6 weeks)1
Reflects downloads up to 10 Oct 2024
Other Metrics
Citations
Cited By
View allView Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in