Grouping Medical Record Data By Type Diseases With K-Means Algorithm


  • Remonaldi Purba STIKOM Tunas Bangsa



Medical Records, Types of Diseases, K-Means, Clustering


Health is a very valuable thing for human life, because anyone can be affected by health problems without realizing what causes it. People who pay less attention to their health are more likely to get sick. Lack of awareness in protecting and preserving the environment will lead to the rapid spread of disease. Efforts in disease prevention are needed by increasing public awareness about the importance of clean and healthy living behavior. In the application of the k-means algorithm for data processing in finding medical record files in the form of notes and documents about patient identity, examination, treatment, and other service actions given to patients. Clustering is a data analysis method that performs the modeling process without supervision (unsupervised) is also a method that performs data grouping with a partition system. The result is grouping using K-Means Clustering which can help in grouping by type of disease and age, the results are divided into children and toddlers, young and adults, old and elderly.


I. Conference, S. Science, and M. Program, “( IN ISNTALASI EMERGENCY ( IGD ) ( Study of Policy Implementation based on Regional Regulation Number 02 of 2014 concerning minimum service standards at Meloy Sangatta Hospital , East Kutai Regency ) Hartati,” pp. 1–5, 2020.

A. L. Hartzler, L. Tuzzio, C. Hsu, and E. H. Wagner, “Roles and Functions of Community Health Workers in Primary Care,” Ann. Fam. Med., vol. 16, no. 3, pp. 240–245, May 2018, doi: 10.1370/afm.2208.

N. Lunt, R. Smith, M. Exworthy, T. Stephen, D. Horsfall, and R. Mannion, “Medical Tourism : Treatments , Markets and Health System Implications : scoping review,” Dir. Employment, Labour Soc. Aff., pp. 1–55, 2011.

A. A. Aprilia Lestari, “Increasing Accuracy of C4 . 5 Algorithm Using Information Gain Ratio and Adaboost for Classification of Chronic Kidney Disease,” J. Soft Comput. Explor., vol. 1, no. 1, pp. 32–38, 2020.

A. B. U. Nájera and J. de la Calleja Mora, “Brief review of educational applications using data mining and machine learning,” Rev. Electron. Investig. Educ., vol. 19, no. 4, pp. 84–96, 2017, doi: 10.24320/redie.2017.19.4.1305.

A. Twin, “Data Mining Data mining,” Min. Massive Datasets, vol. 2, no. January 2013, pp. 5–20, 2005.

V. Marriboyina and L. C. Reddy, “A Review on Data mining from Past to the Future,” Int. J. Comput. Appl., vol. 15, Feb. 2011, doi: 10.5120/1961-2623.

U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AI Mag., vol. 17, no. 3, pp. 37–53, 1996.

A. M. H. Pardede et al., “Smart Health Model with A Linear Integer Programming Approach,” 2019, doi: 10.1088/1742-6596/1361/1/012069.

N. A. Khairani and E. Sutoyo, “Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province,” Int. J. Adv. Data Inf. Syst., vol. 1, no. 1, pp. 9–16, 2020, doi: 10.25008/ijadis.v1i1.13.

M. Omran, A. Engelbrecht, and A. Salman, “An overview of clustering methods,” Intell. Data Anal., vol. 11, pp. 583–605, Nov. 2007, doi: 10.3233/IDA-2007-11602.

S. S. Nagari and L. Inayati, “Implementation of Clustering Using K-Means Method To Determine Nutritional Status,” J. Biometrika dan Kependud., vol. 9, no. 1, p. 62, 2020, doi: 10.20473/jbk.v9i1.2020.62-68.

I. D. Nirmala and P. D. Atika, “Implementation of K-Means Algorithm As a Clustering Method for Selecting Achievement Students Based on Academic Grade,” J. Pilar Nusa Mandiri, no. Ningrum 2009, pp. 199–204, 2020, doi: 10.33480/pilar.v16i2.1575.

A. M. H. Pardede, N. Novriyenni, and L. A. N. Kadim, “Emergency patient health service simulation as a supporter of smart health care,” 2020, doi: 10.1088/1757-899X/725/1/012084.




How to Cite

Remonaldi Purba. (2022). Grouping Medical Record Data By Type Diseases With K-Means Algorithm. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 1(2), 128–134.