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ClusterMix K-Prototypes Algorithm to Capture Variable Characteristics of Patient Mortality With Heart Failure
Corresponding Author(s) : Hardianto Wibowo
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 6, No. 2, May 2021
Abstract
Cardiovascular Disease (CVD) is one of the leading causes of many death worldwide, leading to heart failure incidence. The World Health Organization (WHO) says the number of people dying from cardiovascular disease from heart failure each year has an average of 17,9 million deaths each year, about 31 percent of the total deaths globally. Identify the mortality factors of heart failure patients that need to be formed, which reduces death due to heart failure. One of them is by using variable mortality due to heart failure by applying the k-prototypes algorithm. The clustering result is formed 2 clusters that are considered optimal based on the highest silhouette coefficient value of 0,5777. The results of the study were carried out as segmentation of patients with variable mortality of heart failure patients, which showed that cluster 1 is a cluster of patients who have a low risk of the chance of mortality due to heart failure and cluster 2 is a cluster of patients with a high risk of mortality due to heart failure. The segmentation is based on the average value of each variable of heart failure mortality factor in each cluster compared to normal conditions in serum creatine variables, ejection fraction, age, serum sodium, blood pressure, anemia, creatinine phosphokinase, platelets, smoking, gender, and diabetes.
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- 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. https://doi.org/10.1016/j.rec.2013.04.009
- World Health Organization, “WHO.”
- A. B. I. NATIONAL HEART, LUNG, “No Title.”
- 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. https://doi.org/10.1371/journal.pone.0181001
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- 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, pp. S381, 2019. https://doi.org/10.1016/j.healun.2019.01.971
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- 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, pp. 16, 2020. https://dx.doi.org/10.1186%2Fs12911-020-1023-5
- 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. https://doi.org/10.1109/TBME.2009.2035102
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- J. Supranto, “Statistik Deskriptif.” Jakarta: Airlangga, 1988.
- 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.
- 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. https://doi.org/10.1016/0377-0427(87)90125-7
- 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.
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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. https://doi.org/10.1016/j.rec.2013.04.009
World Health Organization, “WHO.”
A. B. I. NATIONAL HEART, LUNG, “No Title.”
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. https://doi.org/10.1371/journal.pone.0181001
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. https://doi.org/10.1136/bmjopen-2018-023724
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, pp. S381, 2019. https://doi.org/10.1016/j.healun.2019.01.971
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. https://dx.doi.org/10.1002%2Fehf2.12439
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, pp. 16, 2020. https://dx.doi.org/10.1186%2Fs12911-020-1023-5
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. https://doi.org/10.1109/TBME.2009.2035102
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. https://doi.org/10.1016/j.procs.2016.02.095
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. https://doi.org/10.1007/978-3-642-22555-0_48
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. https://doi.org/10.1007/978-3-319-03095-1_15
J. Supranto, “Statistik Deskriptif.” Jakarta: Airlangga, 1988.
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.
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. https://doi.org/10.1016/0377-0427(87)90125-7
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. https://doi.org/10.1137/1.9780898718348.ch9