Algoritma ClusterMix K-Prototypes Untuk Menangkap Karakteristik Pasien Berdasarkan Variabel Penciri Mortalitas Pasien Dengan Gagal Jantung

Raditya Novidianto, Kartika Fithriasari

Abstract


Cardiovascular Disease  (CVD) atau penyakit kardiovaskular adalah salah satu penyebab utama kematian cukup besar di seluruh dunia yang berujung pada kejadian gagal jantung. Organiasasi kesehatan WHO menyebutkan jumlah orang yang  meninggal karena penyakit kardiovaskuler akibat gagal jantung setiap tahun memiliki rata-rata 17,9 juta kematian setiap tahunnya, yaitu sekitar 31 persen dari total kematian secara global. Pendeteksian faktor mortalitas pasien gagal jantung perlu dibentuk segmentasi yang berguna untuk memperkecil peluang terjadinya kematian akibat  gagal jantung. Salah satunya dengan menggunakan variabel penciri mortalitas akibat gagal jantung dengan cara menerapkan algoritma k-prototypes. Hasil penggerombolan terbentuk 2 kluster yang dianggap optimal berdasarkan nilai koefisien silhouette tertinggi yaitu sebesar 0.5777. Hasil penelitian dilakukan segementasi pasien dengan variabel penciri mortalitas pasien gagal jantung yang menunjukan bahwa kluster 1 merupakan gerombol pasien yang memiliki resiko rendah terhadap peluang mortalitas akibat gagal jantung dan kluster 2 merupakan gerombol pasien dengan karaktistik pasien dengan resiko yang tinggi terhadap peluang mortalitas akibat gagal jantung. Segementasi tersebut didasari dari nilai rata-rata setiap variabel penciri  dari faktor mortalitas gagal jantung pada setiap kluster yang dibandingkan dengan kondisi normal pada variabel serum creatine, ejection fraction, usia, serum sodium, tekanan darah, anemia, creatinine phosphokinase, plateles, merokok, jenis kelamin dan diabetes.

Keywords


Penyakit kardiovakuler, ClusterMix, Algoritma K-Prototype, Koefisien Silhouette

Full Text:

PDF

References


J. Barallobre-Barreiro, Y.-L. Chung, and M. Mayr, “Proteomics and metabolomics for mechanistic insights and biomarker discovery in cardiovascular disease,” Rev. Española Cardiol. (English Ed., vol. 66, no. 8, pp. 657–661, 2013.

World Health Organization, “WHO.” https://www.who.int/cardiovascular_diseases/world-heart-day/en/ (accessed Jan. 07, 2020).

A. B. I. NATIONAL HEART, LUNG, “No Title.” https://www.nhlbi.nih.gov/health-topics/heart-failure (accessed Jan. 08, 2020).

T. Ahmad, A. Munir, S. H. Bhatti, M. Aftab, and M. A. Raza, “Survival analysis of heart failure patients: A case study,” PLoS One, vol. 12, no. 7, p. e0181001, 2017.

F. Meng et al., “Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China,” BMJ Open, vol. 9, no. 5, p. e023724, 2019.

T. A. Buchan et al., “Physician prediction versus model predicted prognosis in ambulatory patients with heart failure,” J. Hear. Lung Transplant., vol. 38, no. 4, p. S381, 2019.

B. Chapman, A. D. DeVore, R. J. Mentz, and M. Metra, “Clinical profiles in acute heart failure: an urgent need for a new approach,” ESC Hear. Fail., vol. 6, no. 3, pp. 464–474, 2019.

L. Chiodo, M. Casula, E. Tragni, A. Baragetti, D. Norata, and A. L. Catapano, “Profilo cardiometabolico in una coorte lombarda: lo studio PLIC. Cardio-metabolic profile in a cohort from Lombardy region: the PLIC study,” G. Ital. di Farm. e Farm., vol. 9, no. 2, pp. 35–53, 2017.

D. Chicco and G. Jurman, “Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone,” BMC Med. Inform. Decis. Mak., vol. 20, no. 1, p. 16, 2020.

Y. Al-Kofahi, W. Lassoued, W. Lee, and B. Roysam, “Improved automatic detection and segmentation of cell nuclei in histopathology images,” IEEE Trans. Biomed. Eng., vol. 57, no. 4, pp. 841–852, 2009.

P. Arora and S. Varshney, “Analysis of k-means and k-medoids algorithm for big data,” Procedia Comput. Sci., vol. 78, pp. 507–512, 2016.

T. S. Madhulatha, “Comparison between k-means and k-medoids clustering algorithms,” in International Conference on Advances in Computing and Information Technology, 2011, pp. 472–481.

R. Madhuri, M. R. Murty, J. V. R. Murthy, P. P. Reddy, and S. C. Satapathy, “Cluster analysis on different data sets using K-modes and K-prototype algorithms,” in ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol II, 2014, pp. 137–144.

J. Supranto, “Statistik Deskriptif.” Jakarta: Airlangga, 1988.

A. A. Mattjik, I. Sumertajaya, G. N. A. Wibawa, and A. F. Hadi, “Sidik peubah ganda dengan menggunakan SAS.” 2011.

S. Sharma and S. Sharma, “Applied multivariate techniques,” 1996.

S. G. Rao and A. Govardhan, “Performance validation of the modified k-means clustering algorithm clusters data,” Int. J. Sci. Eng. Res., vol. 6, no. 10, pp. 726–730, 2015.

Z. Ansari, M. F. Azeem, W. Ahmed, and A. V. Babu, “Quantitative evaluation of performance and validity indices for clustering the web navigational sessions,” arXiv Prepr. arXiv1507.03340, 2015.

P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, pp. 53–65, 1987.

N. J. Salkind, Encyclopedia of measurement and statistics. SAGE publications, 2006.

Z. Huang, “Extensions to the k-means algorithm for clustering large data sets with categorical values,” Data Min. Knowl. Discov., vol. 2, no. 3, pp. 283–304, 1998.

R. A. Johnson and D. W. Wichern, Applied multivariate statistical analysis, vol. 5, no. 8. Prentice hall Upper Saddle River, NJ, 2002.

G. Gan, C. Ma, and W. Jianhong, “Center-based clustering algorithms,” Data Clust. Theory, Algorithms Appl., 2007.

T. Ahmad, A. Munir, S. H. Bhatti, M. Aftab, and M. A. Raza, “Survival analysis of heart failure patients: A case study,” PloS one, 2017. https://plos.figshare.com/articles/dataset/Survival_analysis_of_heart_failure_patients_A_case_study/5227684 (accessed Jan. 08, 2020).




DOI: http://dx.doi.org/10.12962/j27213862.v4i1.8479

Refbacks

  • There are currently no refbacks.




Creative Commons License
Inferensi by Department of Statistics ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/inferensi.

ISSN:  0216-308X

e-ISSN: 2721-3862

Web
Analytics Made Easy - StatCounter View My Stats