ANALISA KETERKAITAN RISK FACTOR STROKE DENGAN JENIS STROKE YANG DIDERITA MENGGUNAKAN ALGORITMA ECLAT

Rio Fernando, Lia Anggraini, Alwis Nazir

Abstract


Stroke adalah salah satu penyakit yang sangat mematikan, namun kasus kematian yang disebabkan oleh stroke dapat diperkecil apabila kita mengetahui keterkaitan antara risk factor stroke dengan jenis stroke yang akan diderita. Penelitian ini menggunakan algoritma ECLAT untuk menganalisa keterkaitan antara risk factor stroke dengan jenis stroke yang akan diderita pasien. Data yang digunakan berdasarkan data rekam medis pasien stroke di RSUD Puri Husada Tembilahan dari tahun 2011 hingga tahun 2015 dengan total record sebanyak 700 record. Hasil analisa keterkaitan yang didapatkan dengan menggunakan tools R menghasilkan beberapa rule utama dengan nilai support tertinggi sebesar 41% untuk diagnosa stroke iskemik dan 14% untuk diagnosa stroke hemoragik. Tingkat akurasi hasil analisa tersebut menghasilkan nilai akurasi tertinggi sebesar 97.23% dan terendah sebesar 6.66%.


Full Text:

PDF

References


Yigit, M. O. (2016). The relationship between anemia and recurrence of ischemic stroke in patients with Trousseau's syndrome: A retrospective cross-sectional study. Turkish Journal of Emergency Medicine.

Meschia, J. F., & Bushnell, C. M.-C. (2014). Guidelines for the Primary Prevention of Stroke. A Statement for Healthcare Professionals From the American.

Ohara, T. M. (2016). Rapid Identification of Type A Aortic Dissection as a Cause of Acute Ischemic Stroke. Journal of Stroke and Cerebrovascular Diseases.

Arslan, A. K. (2016). Different medical data mining approaches based prediction of ischemic stroke. Computer Methods and Programs in Biomedicine.

Hao, W. A. (2014). The LDL-HDL Profile Determines the Risk of Atherosclerosis: A Mathematical Model. Mathematical Biosciences Institute and the National Science Foundation Journal.

Al Essa, A. R. (2014). Data Mining and Warehousing. American Society for Engineering Education (ASEE Zone 1) Journal.

Angueraa, A. J. (2016). Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry. Computational and Structural Biotechnology Journal.

Caplan, L. R. (2016). Etiology, classification, and epidemiology of stroke. http://www.uptodate.com/contents/etiology-classification-and-epidemiology-of-stroke. 27 Juni 2016.

de Rada, D., & Martín, M. (2014). Random Route and Quota Sampling: Do They Offer Any Advantage over Probably Sampling Methods?. Open Journal of Statistics.

de Winter, J. (2013). Using the Student’s t-test with extremely small sample sizes. Practical Assessment, Research & Evaluation: A peer-reviewed electronic journal.

Gupta, D. A. (2013). Mining Association Rules from Infrequent Itemsets: A Survey. International Journal of Innovative Research in Science, Engineering and Technology.

Hatano, S. A. (1980). Cerebrovascular disease in the community: results of a WHO collaborative study. Bull World Health Organ.

Ihaka, R., & Gentleman, R. (1996). R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics.

Jellinger, P. S. (2012). American Association of Clinical Endocrinologists' Guidelines for Management of Dyslipidemia and Prevention of Atherosclerosis. AACE Lipid and Atherosclerosis Guidelines, Endocr Practice (Volume 18).

Kaur, M. U. (2014). ECLAT Algorithm for Frequent Itemsets Generation. International Journal of Computer Systems.

Kish, L. (1998). On Quota Sampling. Working Paper, Universidad de Michigan.

Koenig, K. (2015). Introduction to Data Mining. http://www.slideshare.net/AgentK/introduction-to-data-mining-54350352. 27 Juni 2016.

Ramaraj, E., & Venkatesan, N. (2009). An Efficient Pattern Mining Analysis in Health Care Database. Journal of Theoretical & Applied Information Technology.

Rothman, J., & Dawn, M. (1989). Statisticians Can Be Creative Too. Journal of the Market Research Society.

Saxena, A. S. (2014). A Survey on frequent pattern mining methods - Apriori, Eclat, FP growth. International Journal of Engineering Development and Research.

Schmidt-Thieme, L. (2003). Algorithmic Features of Eclat. University of Freiburg's Institute for Computer Science's Journal.

Syvajarvi, A. (2010). Data Mining in Public and Private Sectors: Organizational and Government Applications. New York: Hersey.

United Nations' Statistical Office. (1982) Provisional Guidelines on Standard International Age Classifications. New York: United Nations.

Vijayarani, D. S. (2013). An Efficient Algorithm for Mining Frequent Items in Data Streams. International Journal of Innovative Research in Computer and Communication Engineering.

Zhao, Y. (2013). R and Data Mining: Examples and Case Studies. Elsevier.


Refbacks

  • There are currently no refbacks.


Fakultas Sains dan Teknologi

UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM 18 No. 155 Pekanbaru Riau Indonesia

Website: http://fst.uin-suska.ac.id

Email: sntiki@uin-suska.ac.id