Peningkatan Akurasi Temu Kembali Pengetahuan pada Kasus Sakit Kepala Menggunakan Integrasi Case-Based Reasoning dan Natural Language Processing
DOI:
https://doi.org/10.14421/jiska.6189Keywords:
Headache, Natural Language Processing, Case-Based Reasoning, Random Forest, Data AugmentationAbstract
Headache disorders represent a significant global neurological challenge, yet medical diagnosis is often hindered by subjective patient complaints and limited facilities. This study proposes an integrated model combining Natural Language Processing (NLP) and Case-Based Reasoning (CBR) to enhance the accuracy of medical decision-making for headache cases. The model utilizes the Random Forest algorithm for NLP classification and Cosine Similarity within the CBR framework to identify case relevance. The dataset consists of medical records for two types of headaches: Cluster headache (G44.0) and Tension-type headache (G44.2). Experimental results demonstrate that data augmentation significantly improves model performance, increasing accuracy from 62% to 69%. For the G44.0 label, the model achieved a precision of 0.84, while the G44.2 label reached a recall of 0.87. Furthermore, the CBR system strengthens the diagnosis with a similarity level of up to 0.82 and continuous learning capabilities through the Retain stage. This integration effectively provides faster, more targeted diagnostic recommendations for medical professionals.
References
Amin, K., Kapetanakis, S., Polatidis, N., Althoff, K.-D., & Dengel, A. (2020). DeepKAF: A Heterogeneous CBR Deep Learning Approach for NLP Prototyping. In M. Ivanovic, T. Yildirim, G. Trajcevski, C. Badica, L. Bellatreche, I. Kotenko, A. Badica, B. Erkmen, & M. Savic (Eds.), INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/INISTA49547.2020.9194679
Ardiansyah, A., Ferdiana, R., & Permanasari, A. E. (2022). Optimizing complexity weight parameter of use case points estimation using particle swarm optimization. International Journal of Advances in Intelligent Informatics, 8(2), 165. https://doi.org/10.26555/ijain.v8i2.811
C, L., S, P., Kashyap, A. H., Rahaman, A., Niranjan, S., & Niranjan, V. (2023). Novel Biomarker Prediction for Lung Cancer Using Random Forest Classifiers. Cancer Informatics, 22. https://doi.org/10.1177/11769351231167992
Canizales-Martinez, J. P., Ochoa-Zezzatti, A., & Villar-Patiño, C. (2025). Alarm Recommendation Intelligent System for Multilayer Ceramic Capacitor (MLCC) Electroplating Using Case-Based Reasoning and Natural Language Processing. Lecture Notes in Computer Science, 15464 LNAI, 248–271. https://doi.org/10.1007/978-3-031-83879-8_18
Caro-Martínez, M., Recio-García, J. A., Díaz-Agudo, B., Darias, J. M., Wiratunga, N., Martin, K., Wijekoon, A., Nkisi-Orji, I., Corsar, D., Pradeep, P., Bridge, D., & Liret, A. (2024). iSee: A case-based reasoning platform for the design of explanation experiences. Knowledge-Based Systems, 302. https://doi.org/10.1016/j.knosys.2024.112305
Dai, Z. (2025). Analysis of college students’ physical education learning effect and quality based on text data fusion. Advances in Continuous and Discrete Models, 2025(1), 64. https://doi.org/10.1186/s13662-025-03925-9
Dissing, K. B., Jensen, R. K., Christensen, H. W., Jensen, M. E., & Lauridsen, H. H. (2025). Development and preliminary validation of the Danish headache questionnaire. Chiropractic and Manual Therapies, 33(1). https://doi.org/10.1186/s12998-025-00573-4
Ezzat, M. (2020). A Framework for a Comprehensive Conceptualization of Urban Constructs. In D. Holzer, W. Nakapan, A. Globa, & I. Koh (Eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2020 (Vol. 2, pp. 111–120). The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091286980&partnerID=40&md5=6481b239274ce5a522c91a20a662588e
Feng, Z., Hu, G., Li, B., & Wang, J. (2025). Unleashing the power of ChatGPT in finance research: opportunities and challenges. Financial Innovation, 11(1). https://doi.org/10.1186/s40854-025-00770-3
Galanis, N.-I., & Papakostas, G. A. (2022). An update on cooking recipe generation with Machine Learning and Natural Language Processing. In G. S. Tomar & J. Bansal (Eds.), Proceedings - 2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022 (pp. 739–744). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/AIC55036.2022.9848929
Gao, J., Giunchiglia, F., Zhao, T., Zhao, X., Li, C., & Xu, H. (2025). Oracle bone heritage data augmentation based on two-stage decomposition GANs. Npj Heritage Science, 13(1), 230. https://doi.org/10.1038/s40494-025-01774-z
García-Azorín, D., Farid-Zahran, M., Gutiérrez-Sánchez, M., González-García, M. N., Guerrero, A. L., & Porta-Etessam, J. (2020). Tension-type headache in the Emergency Department Diagnosis and misdiagnosis: The TEDDi study. Scientific Reports, 10(2446). https://doi.org/10.1038/s41598-020-59171-4
Guadarrama Rios, M., Zamberlan, F., Mavromoustakos Blom, P., & Rankovic, N. (2025). Knowing our choices: unveiling true voting patterns through machine learning (ML) and natural language processing (NLP) in European Parliament. Social Network Analysis and Mining, 15(1), 24. https://doi.org/10.1007/s13278-025-01452-9
Hadj‐Mabrouk, H. (2024). A literature review on the applications of artificial intelligence to European rail transport safety. IET Intelligent Transport Systems, 18(12), 2291–2324. https://doi.org/10.1049/itr2.12587
Jeong, M., Sohn, J., Sung, M., & Kang, J. (2024). Improving medical reasoning through retrieval and self-reflection with retrieval-augmented large language models. Bioinformatics, 40, i119–i129. https://doi.org/10.1093/bioinformatics/btae238
Kawazoe, Y., Tsuchiya, M., Shimamoto, K., Seki, T., Shinohara, E., Yada, S., Wakamiya, S., Imai, S., Aramaki, E., & Hori, S. (2025). Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91187-6
Krüger, M. (2023). Comparative Analysis of Text-Based CBR Algorithms for Cybercrime Profiling Investigations. In M. Leyer, M. Leyer, & J. Wichmann (Eds.), CEUR Workshop Proceedings (Vol. 3630, pp. 347–358). CEUR-WS. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184664902&partnerID=40&md5=6dac1b71a0fc6238fca3c06405478c6b
Mourid, M. R., Oweidat, M., Okesanya, O. J., Abady, E. M. A., Deeba, F., Oboli, V. N., & Alsabri, M. (2025). Pediatric Headache Management in the Emergency Department: A Review of Challenges and Solutions. Current Treatment Options in Pediatrics, 11(1). https://doi.org/10.1007/s40746-025-00325-9
Mulyana, S., Hartati, S., Wardoyo, R., & Subandi. (2021). Utilizing natural language processing in case-based reasoning for diagnosing and managing schizophrenia disorder. ICIC Express Letters, 15(10), 1083–1091. https://doi.org/10.24507/icicel.15.10.1083
Mulyanto, A., Hartati, S., & Wardoyo, R. (2024). An Integrated Model Of Natural Language Processing Technique And Case-Based Reasoning For Supporting Study Program Accreditation. ICIC Express Letters, 18(7), 749–757. https://doi.org/10.24507/icicel.18.07.749
Naqvi, S. M. R., Ghufran, M., Meraghni, S., Varnier, C., Nicod, J.-M., & Zerhouni, N. (2022). CBR-Based Decision Support System for Maintenance Text Using NLP for an Aviation Case Study. In C. Li, G. Valentino, L. Kang, D. Cabrera, & D. Gjorgjevikj (Eds.), Proceedings - 2022 Prognostics and Health Management Conference, PHM-London 2022 (pp. 344–349). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/PHM2022-London52454.2022.00067
Qiu, J., Zhu, T., Qin, K., & Zhang, W. (2025). The interaction network and potential clinical effectiveness of dimensional psychopathology phenotyping based on EMR: a Bayesian network approach. BMC Psychiatry, 25(1). https://doi.org/10.1186/s12888-025-06510-2
Rybinski, K. (2022). Assessing how QAA accreditation reflects student experience. Higher Education Research & Development, 41(3), 898–918. https://doi.org/10.1080/07294360.2021.1872058
Silva, H., António, N., & Bacao, F. (2022). A Rapid Semi-automated Literature Review on Legal Precedents Retrieval. In G. Marreiros, B. Martins, A. Paiva, A. Sardinha, & B. Ribeiro (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): 13566 LNAI (pp. 53–65). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16474-3_5
Tripakornkusol, V., Sinsopa, N., Khamsai, S., & Sawanyawisuth, K. (2025). Phenotypes of headache in patients with obstructive sleep apnea. Scientific Reports, 15(1), 4806. https://doi.org/10.1038/s41598-025-89538-4
Venkatesh Raja, K., Siddharth, R., Yuvaraj, S., & Ramesh Kumar, K. A. (2024). An Artificial Intelligence based automated case-based reasoning (CBR) system for severity investigation and root-cause analysis of road accidents – Comparative analysis with the predictions of ChatGPT. Journal of Engineering Research, 12(4), 895–903. https://doi.org/https://doi.org/10.1016/j.jer.2023.09.019
Villena, F., Bravo-Marquez, F., & Dunstan, J. (2025). NLP modeling recommendations for restricted data availability in clinical settings. BMC Medical Informatics and Decision Making, 25(1), 116. https://doi.org/10.1186/s12911-025-02948-2
Wang, H., Meng, X., & Zhu, X. (2022). Improving knowledge capture and retrieval in the BIM environment: Combining case-based reasoning and natural language processing. Automation in Construction, 139. https://doi.org/10.1016/j.autcon.2022.104317
Wu, Y., Xie, H., Gu, L., Chen, R., Chen, S., Wang, F., Liu, Y., Chen, L., & Tang, J. (2024). Advancing Mental Health Care: Intelligent Assessments and Automated Generation of Personalized Advice via M.I.N.I and RoBERTa. Applied Sciences (Switzerland), 14(9447). https://doi.org/10.3390/app14209447
Yang, B., Lu, W., Xuan, Y., Hao, C., & Huang, X. (2025). The influences of social support expressed from doctors and disclosed from peers on patient decision-making: an analysis from the online health community. Scientific Reports, 15(1), 2703. https://doi.org/10.1038/s41598-024-85023-6
Zhao, Y., Yi, Y., Zhou, H., Pang, Q., & Wang, J. (2025). The burden of migraine and tension-type headache in Asia from 1990 to 2021. Journal of Headache and Pain, 26(1). https://doi.org/10.1186/s10194-025-01990-9
Zhu, Y., Duan, J., & Wu, T. (2021). Animal fiber imagery classification using a combination of random forest and deep learning methods. Journal of Engineered Fibers and Fabrics, 16. https://doi.org/10.1177/15589250211009333
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