Prototipe Machine Learning Untuk Prognosis Penyakit Demensia (The Prototype of Machine Learning for The Prognosis of Dementia)

Rifqi Hammad, Julia Kurniasih, Nur Fitrianingsih Hasan, Christin Nandari Dengen, Kusrini Kusrini

Abstract

Alzheimer’s Disease International memperkirakan bahwa jumlah penderita demensia di Indonesia akan meningkat menjadi 2 juta jiwa lebih pada tahun 2030. Kerugian yang diderita oleh Indonesia akibat demensia diproyeksikan mencapai US $ 1,7 miliar per tahun. Ini disebabkan adanya penurunan fungsi kognitif dan aktivitas sosial yang dialami oleh penderita demensia. Akibatnya, muncullah masalah kesehatan masyarakat yang berdampak pada bertambahnya biaya kesehatan. Untuk mengatasi masalah ini, perlu dilakukan penanganan yang tepat. Pengembangan alat bantu prognosis demensia dapat membantu proses tata laksana perawatan dengan lebih tepat. Penelitian ini mengembangkan prototipe machine learning untuk prognosis penyakit demensia menggunakan metode forward chaining. Hasil pengujian memperlihatkan nilai akurasi sebesar 100% yang menyatakan bahwa prognosis sudah sesuai dengan ketentuan pakar.


Keywords


machine learning, prognosis, demensia, forward chaining.


References

Ahmad Aniq Noor Mutsaqof dan Esti Suryani. “Sistem Pakar Untuk Mendiagnosis Penyakit Infeksi Menggunakan Forward Chaining.” Jurnal ITSMART 4, no. 1 (2015): 43-47.

Ana Luiza Dallora, Shahryar Eivazzadeh, Emilia Mendes, Johan Berglund and Peter Anderberg. “Machine Learning and Microsimulation Techniques on The Prognosis of Dementia: A Systematic Literature Review.” Procedia Computer Science 100 (2016): 480-488.

Andreas Holzinger. “Machine Learning for Health Informatics.” Springer International Publishing (2016): 1-24. DOI: 10.1007/978-3-319-50478-0 1.

Andreas K Triantafyllidis and Athanasios Tsanas. “ Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature.” Journal of Medical Internet Research 21, no, 4 (2019): 1-9. doi:10.2196/12286.

Bharti E. Nerkar and Sanjay S. Gharde. “Best Treatment Identification for Disease Using Machine Learning Approach In Relation To Short Text.” IOSR Journal of Computer Engineering (IOSR-JCE) 16, no. 3 (2014): 05-12.

Cecilia Hernqvist and Matilda Rosander. “Machine Learning for Symptoms Quantification of Parkinson’s Disease Patients.” Master’s Thesis. Chalmers University of Technology, 2017.

CNN Indonesia. “Demensia, Mimpi Buruk di Masa Senja.” Diakses pada 28 Mei 2019. https://www.cnnindonesia.com/gaya-hidup/20180921163237-255-332130/demensia-mimpi-buruk-di-masa-senja.

Enrico Pellegrini, Lucia Ballerini, Maria del C. Valdes Hernandez, Francesca M. Chappell, Victor González-Castro, Devasuda Anblagan, Samuel Danso, Susana Muñoz-Maniega, Dominic Job, Cyril Pernet, Grant Mair, Tom J. MacGillivray, Emanuele Trucco, and Joanna M. Wardlawa. “Machine Learning of Neuroimaging For Assisted Diagnosis of Cognitive Impairment and Dementia: A systematic review.” Alzheimers Dement (Amst) 10, (2018): 519–535.

Febby Kesumaningtyas. “Sistem Pakar Diagnosa Penyakit Demensia Menggunakan Metode Forward Chaining.” Jurnal Edik Informatika 3, no. 2 (2017): 95-102.

Fei Jiang, Yong Jiang, Hui Zhi, Yi Dong, Hao Li, Sufeng Ma, Yilong Wang, Qiang Dong, Haipeng Shen, and Yongjun Wang. “Artificial Intelligence in Healthcare: past, present and future.” Stroke Vasc Neurol 2, no. 4(2017): 230–243.

Hisashi Kurasawa, Akinori Fujino and Katsuyoshi Hayashi.“Predicting Patients’ Treatment Behavior By Medical Data Analysis Using Machine Learning Technique.” NTT Technical Review 15, no. 8 (2017): 1-6.

Holger Fröhlich, Rudi Balling, Niko Beerenwinkel, Oliver Kohlbacher, Santosh Kumar, Thomas Lengauer, Marloes H. Maathuis, Yves Moreau, Susan A. Murphy, Teresa M. Przytycka, Michael Rebhan, Hannes Röst, Andreas Schuppert, Matthias Schwab, Rainer Spang, Daniel Stekhoven, Jimeng Sun, Andreas Weber, Daniel Ziemek and Blaz Zupan. “From Hype to Reality: Data Science Enabling Personalized Medicine.” BMC Medicine, (2018): 1-15. https://doi.org/10.1186/s12916-018-1122-7

Jan A. Roth, Manuel Battegay, Fabrice Juchler, Julia E. Vogt and Andreas F. Widmer. “Introduction to Machine Learning in Digital Healthcare Epidemiology.” Infection Control & Hospital Epidemiology 39, no. 12 (2018): 1457-1462.

Jenna Wiens and Erica S. Shenoy. “Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology.” Healthcare Epidemiology, (2018): 149-153.

Kee Yuan Ngiam and Ing Wei Khor. “Big Data and Machine Learning Algorithms For Health-Care Delivery.” The Lancet Oncology’s Digital Oncology Series 20, no. 5 (2019): 262-273.

Kementerian Kesehatan Republik Indonesia. “Menkes: Lansia yang Sehat, Lansia yang Jauh dari Demensia.” Diakses pada 28 Mei 2019. http://www.depkes.go.id/article/view/¬16031000003¬/¬-menkes-lansia-yang-sehat-lansia-yang-jauh-dari-demensia.html.

Konstantina Kourou, Themis P. Exarchos, Konstantinos P Exarchos, Michalis V. Karamouzis and Dimitrios I. Fotiadis. “Machine Learning Applications In Cancer Prognosis and Prediction.” Computational and Structural Biotechnology Journal 13, (2014): 8-17. http://dx.doi.org-/10.1016/j.csbj.2014.11.005.

M. C. Keerrthega and D. Thenmozhi. “Identifying Disease -Treatment Relations Using Machine Learning Approach.” Procedia Computer Science 87, (2016): 306–315.

Muhammad Aurangzeb Ahmad, Carly Eckert, Ankur Teredesai, and Greg McKelvey. “Interpretable Machine Learning in Healthcare.” IEEE Intelligent Informatics Bulletin 19, no. 1 (2018): 1-7.

Niharika G. Maity and Sreerupa Das. “Machine Learning For Improved Diagnosis and Prognosis in Healthcare.” IEEE Aerospace Conference, (2017): 4058-4066. DOI: 10.1109/¬AERO.¬2017.¬7943950 5

Sunil Gupta, Truyen Tran, Wei Luo, Dinh Phung, Richard Lee Kennedy, Adam Broad, David Campbell, David Kipp, Madhu Singh, Mustafa Khasraw, Leigh Matheson, David M Ashley, Svetha Venkatesh. “Machine-Learning Prediction of Cancer Survival: A Retrospective Study Using Electronic Administrative Records and A Cancer Registry.” BMJ Open 4, no. 3 (2014): 1-7. doi:10.1136/bmjopen-2013-004007.

Tariq Ahmad, Lars H. Lund, Pooja Rao, Rohit Ghosh, Prashant Warier, Benjamin Vaccaro, Ulf Dahlstrom, Christopher M. O’Connor, G. Michael Felker and Nihar R. Desai. “Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.” Journal of the American Heart Association 7, no. 8 (2018): 1-14. DOI: 10.1161/JAHA.117.008081.

Windah Supartini dan Hindarto. “Sistem Pakar Berbasis Web Dengan Metode Forward Chaining Dalam Mendiagnosis Dini Penyakit Tuberkulosis di Jawa Timur.” Jurnal KINETIK 1, no. 3 (2016): 147-154, http://kinetik.umm.ac.id/index.php/kinetik/article/view/123/19.




DOI: http://dx.doi.org/10.33164/iptekkom.21.1.2019.17-29

Refbacks

  • There are currently no refbacks.




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

Publisehd by BPSDMP KOMINFO YOGYAKARTA

Balai Pengembangan Sumber Daya Manusia dan Pengkajian Komunikasi dan Informatika Yogyakarta

Jl. Imogiri Barat No. 4 Km. 5, Sewon, Kab. Bantul, DI. Yogyakarta. Indonesia

ph/fax. +62 274 - 375253

Powered by OJS