CLINICAL DECISION SUPPORT SYSTEM BASED ON DECISION TREE ALGORITHM TO CLASSIFY HEART DISEASE

research
  • 08 May
  • 2018

CLINICAL DECISION SUPPORT SYSTEM BASED ON DECISION TREE ALGORITHM TO CLASSIFY HEART DISEASE

Heart disease is the leading cause of death in the world, one of the best ways to reduce the death rate is by detect the symptoms in the early stages. Hospital information systems rarely provide a decision support system that can be used to detect early symptoms, most systems are designed only to support the payment of bills for patients, inventory management and also a simple statistical information, to overcome the problem, it can be used a computer-based information or clinical decision support systems. This study aims to build a clinical decision support system to identify whether a patient affected by heart disease or not by using a decision tree algorithm. System built using the rules generated by the decision tree algorithm as many as 75 rules, results show that the system has been built can be used as a way to detect early symptoms of heart disease.

Unduhan

 

REFERENSI

[1]    M. Wahyudi dan S. N. N. Alfisahrin, “Komparasi Algoritma C4.5, Naive Bayes, Dan Neural Network Untuk Memprediksi Penyakit Jantung,” Ticom, vol. 2, no. 2, Januari 2014.

[2]    T. D. Pham, H. Wang, X. Zhou, D. Beck, M. Brandl, G. Hoehn, J. Azok, M.-L. Brennan, S. L. Hazen, K. Li dan S. T. C. Wong, “Computational Prediction Models for Early Detection of Risk of Cardiovascular Events Using Mass Spectrometry Data,”         IEEE         TRANSACTIONS        ON INFORMATION TECHNOLOGY IN BIOMEDICINE, vol. 12, no. 5, pp. 636-643, September 2008.

[3]    G. Subbalakshmi, K.  Ramesh dan M.  C. Rao, “Decision Support in  Heart Disease Prediction System using Naive Bayes,” Indian Journal of Computer Science and Engineering, vol. Vol. 2, no. No. 2 , Apr-May 2011.

[4]   A. Gupta dan R. Sharda, “Improving the science of healthcare delivery and informatics using modeling  approaches,”  Decision  Support Systems, vol. Volume 55, no. Issue 2, pp. Pages 423-427, 2012.

[5]   K. Srinivas, B. K. Rani dan A. Govrdhan, “Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks,” International Journal on Computer Science and Engineering, vol. 02, no. 02, pp. 250-254, 2010.

[6]   E. S. Berner dan T. J. L. Lande, “Overview of Clinical Decision Support Systems,” dalam Clinical Decision Support Systems Theory and Practice, New York, Springer, 2007, pp. 3-22.

[7]    I. Kononenko, “Machine Learning for  Medical Diagnosis: history, state  of the  art, and perspektif,” Artificial Intelligence in Medicine, vol. 23, pp. 89-109, 2001.

[8]    I. Sim, P. Gorman, R. A. Greenes, R. B. Haynes, B. Kaplan, H. Lehmann dan P. C. Tang, “Clinical   Decision Support Systems for the Practice of Evidence-based Medicine,” Journal of the American Medical Informatics Association, vol.8, no. 6, p. 527–534, Nov-Dec 2001.

[9]    B. Keltch, Y. Lin dan C. Bayrak, “Advanced decision support for complex clinical decisions,” J. Biomedical Science and Engineering, pp. 509-516, May 2010.

[10] A.  P.  K.,  “Clinical  Decision  Support  System: Risk Level Prediction Of Heart Disease Using Decision Tree Fuzzy Rules,” Asian Transactions on Computers, vol. 2, no. 4, September 2012.

[11] J. M. Hardin dan D. C. Chhieng, “Data Mining and Clinical Decision Support Systems,” dalam Clinical Decision Support Systems Theory and Practice Second Edition, USA, Springer Science, 2007, p. 44.

[12] I.  H.  Witten,  E.  Frank  dan  M.  A.  Hall,  Data Mining  Practical  Machine  Learning Tools  and Techniques Third Edition, United States: Morgan Kaufmann, 2011.

[13] M. F. Hornick, E. Marcadé dan S. Venkayala, Java   Data   Mining:   Strategy   Standard,   and Practice A Practical Guide for Architecture, Design, and Implementation, San Francisco: Morgan Kaufmann, 2007.

[14] O.  Maimon  dan  L.  Rokach,  Data  Mining  and Knowledge  Discovery  Handbook  Second Edition, New York: Springer, 2010.

[15] B.   d.   Ville,   Decision   Trees   for   Business Intelligence and Data Mining Using SAS Enterprise Miner, United States of America: SAS Publishing,

Cari

Bidang Ilmu

Kategori

Kiriman Terbaru