Implementasi Machine Learning Tanpa Label (Unsupervised) dalam Identifikasi dan Klasifikasi Penyakit Berdasarkan Data Medis Pasien

Pradithia Jody, Muhamad Yusuf Sucahyo, Rizqi Setiawan, Dwi Bagus Prasetyo, Fachri Amsury, Riza Fahlapi

Abstract


This study aims to implement an unsupervised learning method using the K-Means Clustering algorithm to group patients based on medical data without requiring prior disease labels. The dataset used consists of 300 simulated patient data (synthetic data) with variables of blood pressure, blood sugar, cholesterol, and symptoms of fever, cough, shortness of breath, and muscle pain. The results show that the model can divide patients into four main clusters: hypertension, diabetes, hypercholesterolemia, and respiratory infections, which are consistent with realistic clinical conditions. Analysis of the average feature per cluster, scatter plots, and heatmaps strengthen the interpretation of the characteristics of each group. This approach proves that the K-Means method can be an efficient early diagnostic tool even though the data is unlabeled.

Keywords


Diabetes, Hypertension, Hypercholesterolemia, Respiratory Infection, Unsupervised Learning.

Full Text:

PDF

References


Ahsan, M. M., Siddique, Z., 2022. Machine learning-based heart disease diagnosis: A systematic literature review. Artificial Intelligence in Medicine, 128(Ml).

Arbi, H. A., Putri, R. A., 2023. Visualisasi Data Pemetaan Daerah Hipertensi Menggunakan Algoritma K-Means. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 6(4), 631–638.

Capó, M., Pérez, A., Lozano, J. A., 2020. An efficient K-means clustering algorithm for tall data. Data Mining and Knowledge Discovery, 34(3), 776–811.

Chushig-Muzo, D., Soguero-Ruiz, C., Engelbrecht, A. P., De Miguel Bohoyo, P., Mora-Jimenez, I., 2020. Data-Driven Visual Characterization of Patient Health-Status Using Electronic Health Records and Self-Organizing Maps. IEEE Access, 8, 137019–137031.

Dash, S. S., Nayak, S. K., Mishra, D., 2021. A review on machine learning algorithms. Smart Innovation, Systems and Technologies, 153(May), 495–507.

Debener, J., Heinke, V., Kriebel, J., 2023. Detecting insurance fraud using supervised and unsupervised machine learning. Journal of Risk and Insurance, 90(3), 743–768.

Engle, S., Whalen, S., Joshi, A., Pollard, K. S., 2020. Unboxing cluster heatmaps. BMC Bioinformatics, 18(Suppl 2), 1–15.

Fernandez, N. F., Gundersen, G. W., Rahman, A., Grimes, M. L., Rikova, K., Hornbeck, P., Maayan, A., 2020. Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data. Scientific Data, 4, 1–12.

Gu, Z., 2022. Complex heatmap visualization. IMeta, 1(3), 1–15.

Hu, Y., Yan, H., Liu, M., Gao, J., Xie, L., Zhang, C., Wei, L., Ding, Y., Jiang, H., 2024. Detecting cardiovascular diseases using unsupervised machine learning clustering based on electronic medical records. BMC Medical Research Methodology, 24(1).

Janiesch, C., Zschech, P., Heinrich, K., 2021. Machine Learning and Deep Learning. Electronic Markets, 31, 685–695.

Kumar, K. J., Jairam, K., Ambedkar, C., 2023. An In-Depth Study Of Machine Learning In Artificial Intelligence. Educational Administration: Theory and Practice, 29(4), 2401–2408.

Kumar, Y., Koul, A., Singla, R., Ijaz, M. F., 2023. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing, 14(7), 8459–8486.

Liu, M., Li, M., Zhang, X., 2022. The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents. Computational Intelligence and Neuroscience, 2022.

Liu, Y., Ma, S., Du, X., 2024. A Novel Effective Distance Measure and a Relevant Algorithm for Optimizing the Initial Cluster Centroids of K-means. IEEE Access.

Myszczynska, M. A., Ojamies, P. N., Lacoste, A. M. B., Neil, D., Saffari, A., Mead, R., Hautbergue, G. M., Holbrook, J. D., Ferraiuolo, L., 2020. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nature Reviews Neurology, 16(8), 440–456.

Qin, J., Chen, L., Liu, Y., Liu, C., Feng, C., Chen, B., 2020. A machine learning methodology for diagnosing chronic kidney disease. IEEE Access, 8, 20991–21002.

Richens, J. G., Lee, C. M., Johri, S., 2020. Improving the accuracy of medical diagnosis with causal machine learning. Nature Communications, 11(1), 1–9.

Sharma, R., 2020. Study of Supervised Learning and Unsupervised Learning. International Journal for Research in Applied Science and Engineering Technology, 8(6), 588–593.

Wala, J., Herman, H., Umar, R., Suwanti, S., 2024. Heart Disease Clustering Modeling Using a Combination of the K-Means Clustering Algorithm and the Elbow Method. Scientific Journal of Informatics, 11(4), 903–914.

Xu, Y., 2025. Research on Computer Information Network Security Technology and Development Direction. Journal of Computing and Electronic Information Management, 16(2), 21–24.

Yu, C. S., Lin, C. H., Lin, Y. J., Lin, S. Y., Wang, S. Te, Wu, J. L., Tsai, M. H., Chang, S. S., 2020. Clustering heatmap for visualizing and exploring complex and high-dimensional data related to chronic kidney disease. Journal of Clinical Medicine, 9(2).

Zubair, M., Iqbal, M. A., Shil, A., Chowdhury, M. J. M., Moni, M. A., Sarker, I. H., 2024. An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling. Annals of Data Science, 11(5), 1525–1544.




DOI: http://dx.doi.org/10.33087/jiubj.v26i1.6402

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

ADRESS JOURNAL

JURNAL ILMIAH UNIVERSITAS BATANGHARI JAMBI (JIUBJ)
Published by Lembaga Penelitian dan Pengabdian kepada Masyarakat
Adress: Jl.Slamet Ryadi, Broni-Jambi, Kec.Telanaipura, Kodepos: 36122, email: jiubj.unbari@gmail.com, Phone: 0741-670700

Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.