Machine Learning untuk Identifikasi Jenis Kanker Darah (Leukemia)
DOI:
https://doi.org/10.30736/voj.v4i1.493Keywords:
Kanker Darah, Leukemia, Machine Learning, DNA, RNAAbstract
Metode yang cepat dan tepat untuk membedakan jenis kanker darah sangat penting agar pasien kanker mendapatkan perlakuan yang sesauai. Pada penelitian ini identifikasi jenis kanker darah dilakukan dengan memanfaatkan kecerdasan buatan khususnya machine learning. Proses identifikasi kanker leukemia menggunakan machine learning dimulai dengan melakukan ektraksi ciri. Proses ektraksi ciri dilakukan dengan memanfaatkan metode Rantai Markov. Dari proses ini akan membentuk matriks yang kemudian dijadikan data training dan testing pada beberapa algoritma machine learning. Berdasarkan hasil training dan testing diperoleh hasil bahwa akurasi algoritma Decision Tree Classification memberikan hasil terbaik yaitu 83%, disusul dengan metode KNN dan sebesar 50%, sedangkan metode SVM hanya mencapai akurasi 37.5%.Downloads
References
Al Faroby, M. H. Z. (2018). Identifikasi Jenis Kanker Darah (Leukemia) Terhadap Pengaruh Parameter Kernel Support Vector Machine Dan Ekstraksi Ciri Dengan Rantai Markov Orde 2. 30–31.
Amalutfia, S. Y., & Hafiyusholeh, M. (2020). Analisis Peramalan Nilai Tukar Rupiah Terhadap Dollar dan Yuan Menggunakan FTS-Markov Chain. Vygotsky, 2(2), 102. https://doi.org/10.30736/vj.v2i2.258
American Cancer Society. (2019). Facts & Figures 2019. American Cancer Society, 1–76. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2019/cancer-facts-and-figures-2019.pdf
Dese, K., Raj, H., Ayana, G., Yemane, T., Adissu, W., Krishnamoorthy, J., & Kwa, T. (2021). Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images. Clinical Lymphoma, Myeloma and Leukemia, 21(11), e903–e914. https://doi.org/10.1016/j.clml.2021.06.025
Dewi, M. (2017). Sebaran Kanker di Indonesia, Riset Kesehatan Dasar 2007. Indonesian Journal of Cancer, 11(29), 1–8. https://media.neliti.com/media/publications/197251-ID-sebaran-kanker-di-indonesia-riset-keseha.pdf
Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., Spitzer, A. I., & Ramkumar, P. N. (2020). Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Current Reviews in Musculoskeletal Medicine, 13(1), 69–76. https://doi.org/10.1007/s12178-020-09600-8
Kumar, Vinayshekhar Bannihatti; Kumar, Sujay S; Saboo, V. (2016). 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) : 19-21 Sept. 2016. 88–93.
Mahmood, N., Shahid, S., Bakhshi, T., Riaz, S., Ghufran, H., & Yaqoob, M. (2020). Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach. Medical and Biological Engineering and Computing, 58(11), 2631–2640. https://doi.org/10.1007/s11517-020-02245-2
Morrison, C., & Hesdorffer, C. S. (2012). Patients’ Guide to Leukemia (Panduan untuk Penderita Leukemia). In Jakarta: PT Indeks.
Pratiwi, R. Y. (2014). Aplikasi Model Fuzzy Untuk Diagnosa Jenis Kanker Darah (Leukemia).
Shyr, D. C., Zhang, B. M., Parkman, R., & Brewer, S. E. (2020). Machine Learning Methods to Better Predict Post-Hematopoietic Stem Cell Transplant (HSCT) Leukemic Relapse in Pediatric Patients with Acute Lymphoblastic Leukemia: Random Forest (RF) Classification Featuring Serial Post-Transplant Lineage-Specific Chimerism. Blood, 136(Supplement 1), 6–7. https://doi.org/10.1182/blood-2020-139104
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
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