CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES

Ümit Yılmaz; Erol Özçekiç

doi:10.31796/ogummf.1591951

EN TR

CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES

Abstract

Liver diseases pose a significant global health challenge due to their impact on metabolic function and the difficulty of early detection. Traditional diagnostic methods such as liver biopsy have limitations due to their invasive nature and high costs. This research examines the application of advanced machine learning techniques such as Gradient Boosting, AdaBoost, XGBoost and CatBoost for classification of liver diseases using a publicly available dataset of 1700 clinical records. Statistical analyses identified key predictors such as age, body mass index (BMI), lifestyle factors, and liver function tests, which were used to train and evaluate the models. The performance of the models was evaluated using metrics such as accuracy, precision, recall and AUC-ROC. The CatBoost model showed the highest performance with an accuracy of 93.82%, while also producing the most consistent results with precision (91.97%), recall (96.62%), F1 score (94.25%) and AUC-ROC (95.64%). These results highlight the potential of machine learning-based approaches to improve diagnostic accuracy and reduce reliance on invasive procedures. The proposed framework can contribute to improving patient outcomes and optimizing healthcare resources by providing a foundation for real-time clinical decision support systems.

Keywords

Liver disease , Machine learning , Diagnosis , Classification , Boosting

BOOSTING MAKİNE ÖĞRENME YAKLAŞIMLARI İLE KARACİĞER HASTALIKLARININ SINIFLANDIRILMASI

Öz

Karaciğer hastalıkları, metabolik fonksiyonlar üzerindeki etkileri ve erken teşhis zorlukları nedeniyle önemli bir küresel sağlık sorunu oluşturmaktadır. Karaciğer biyopsisi gibi geleneksel tanı yöntemleri, invaziv yapıları ve yüksek maliyetleri nedeniyle sınırlamalar taşımaktadır. Bu araştırma, 1.700 klinik kayıttan oluşan kamuya açık bir veri kümesi kullanarak karaciğer hastalıklarının sınıflandırılması için Gradient Boosting, AdaBoost, XGBoost ve CatBoost gibi gelişmiş makine öğrenmesi tekniklerinin uygulanmasını incelemektedir. İstatistiksel analizler, modelleri eğitmek ve değerlendirmek için kullanılan yaş, vücut kitle endeksi, yaşam tarzı faktörleri ve karaciğer fonksiyon testleri gibi temel belirleyicileri ortaya koymuştur. Modellerin performansı, doğruluk, kesinlik, duyarlılık ve AUC-ROC gibi metrikler kullanılarak değerlendirilmiştir. CatBoost modeli, %93,82 doğruluk oranı ile en yüksek performansı göstermiş, aynı zamanda kesinlik (%91,97), duyarlılık (%96,62), F1 skoru (%94,25) ve AUC-ROC (%95,64) değerleriyle en istikrarlı sonuçları üretmiştir. Bu sonuçlar, makine öğrenmesi tabanlı yaklaşımların tanı doğruluğunu artırma ve invaziv prosedürlere olan bağımlılığı azaltma potansiyelini vurgulamaktadır. Önerilen çerçeve, gerçek zamanlı klinik karar destek sistemleri için bir temel oluşturarak hasta sonuçlarının iyileştirilmesine ve sağlık hizmetleri kaynaklarının optimizasyonuna katkı sağlayabilir.

Anahtar Kelimeler

Karaciğer hastalığı , Makine öğrenmesi , Tanı , Sınıflandırma , Boosting

References

An, Q., Rahman, S., Zhou, J., & Kang, J. J. (2023). A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges. Sensors, 23(9), 4178. https://www.mdpi.com/1424-8220/23/9/4178
Anderson, D., Bjarnadottir, M. V., & Nenova, Z. (2022). Machine Learning in Healthcare: Operational and Financial Impact. In V. Babich, J. R. Birge, & G. Hilary (Eds.), Innovative Technology at the Interface of Finance and Operations: Volume I (pp. 153-174). Springer International Publishing. https://doi.org/10.1007/978-3-030-75729-8_5
Aouragh, A. A., & Bahaj, M. (2023, 16-22 Dec. 2023). Feature Selection and Dimensionality Reduction for Unbalanced Liver Disease Classification with Optimized Machine Learning Algorithms. 2023 7th IEEE Congress on Information Science and Technology (CiSt),
Ayyadevara, V. K. (2018). Gradient Boosting Machine. In Pro Machine Learning Algorithms : A Hands-On Approach to Implementing Algorithms in Python and R (pp. 117-134). Apress. https://doi.org/10.1007/978-1-4842-3564-5_6
Bennett, M., Hayes, K., Kleczyk, E. J., & Mehta, R. (2022). Similarities and differences between machine learning and traditional advanced statistical modeling in healthcare analytics. arXiv preprint arXiv:2201.02469.
Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3), 1937-1967. https://doi.org/10.1007/s10462-020-09896-5
Biau, G., Cadre, B., & Rouvière, L. (2019). Accelerated gradient boosting. Machine Learning, 108(6), 971-992. https://doi.org/10.1007/s10994-019-05787-1
Bozuyla, M. (2021). AdaBoost Ensemble Learning on top of Naive Bayes Algorithm to Discriminate Fake and Genuine News from Social Media [Naive Bayes Algoritmasının AdaBoost Topluluk Öğrenme Modeli ile Sosyal Medyada Sahte ve Gerçek Haberlerinin Ayırt Edilmesi]. European Journal of Science and Technology(28), 459-462. https://doi.org/10.31590/ejosat.1005577
Casotti, V., & D’Antiga, L. (2019). Basic Principles of Liver Physiology. In L. D'Antiga (Ed.), Pediatric Hepatology and Liver Transplantation (pp. 21-39). Springer International Publishing. https://doi.org/10.1007/978-3-319-96400-3_2
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining,

Decharatanachart, P., Chaiteerakij, R., Tiyarattanachai, T., & Treeprasertsuk, S. (2021). Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis. BMC Gastroenterology, 21(1), 10. https://doi.org/10.1186/s12876-020-01585-5
El Kharoua, R. (2024). Predict Liver Disease: 1700 Records Dataset. https://www.kaggle.com/datasets/rabieelkharoua/predict-liver-disease-1700-records-dataset
Ganie, S. M., Dutta Pramanik, P. K., & Zhao, Z. (2024). Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches. BMC Medical Informatics and Decision Making, 24(1), 160. https://doi.org/10.1186/s12911-024-02550-y
Ghosh, S., Chatterjee, A., & Chatterjee, D. (2021). An Improved Load Feature Extraction Technique for Smart Homes Using Fuzzy-Based NILM. IEEE Transactions on Instrumentation and Measurement, 70, 1-9. https://doi.org/10.1109/TIM.2021.3095093
Hidayat, A. (2024). Predictive Modelling of Liver Disease Using Biochemical Markers and K-Nearest Neighbors Algorithm. International Journal of Artificial Intelligence in Medical Issues, 2(2), 104-114.
Hinojosa Lee, M. C., Braet, J., & Springael, J. (2024). Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores. Applied Sciences, 14(21), 9863. https://www.mdpi.com/2076-3417/14/21/9863
Hornyák, O., & Iantovics, L. B. (2023). AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics. Mathematics, 11(8), 1801. https://www.mdpi.com/2227-7390/11/8/1801
Jovović, I., Grebović, M., Pokvić, L. G., Popović, T., & Čakić, S. (2024). Liver Diseases Classification Using Machine Learning Algorithms. In A. Badnjević & L. Gurbeta Pokvić, MEDICON’23 and CMBEBIH’23 Cham.
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. J Ambient Intell Humaniz Comput, 14(7), 8459-8486. https://doi.org/10.1007/s12652-021-03612-z
Luo, J., Wei, Z., Man, J., & Xu, S. (2023). TRBoost: a generic gradient boosting machine based on trust-region method. Applied Intelligence, 53(22), 27876-27891. https://doi.org/10.1007/s10489-023-05000-w
Makkena, K. R., & Natarajan, K. (2023). Classification Algorithms for Liver Epidemic Identification. EAI Endorsed Transactions on Pervasive Health and Technology, 9. https://doi.org/10.4108/eetpht.9.4379
Mathur, S., Karodi, P., & Dhanare, R. (2024, 13-14 March 2024). Fatty Liver Disease Prediction Through Machine Learning. 2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL),
Mohamed, S., Ezzat, R., Ghorab, S., Bhatnagar, R., & Shams, M. Y. (2023, 1-3 Nov. 2023). Liver Disease Identification Based on Machine Learning Algorithms. 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS),
Pei, X., Zuo, K., Li, Y., & Pang, Z. (2023). A Review of the Application of Multi-modal Deep Learning in Medicine: Bibliometrics and Future Directions. International Journal of Computational Intelligence Systems, 16(1), 44. https://doi.org/10.1007/s44196-023-00225-6
Pilankar, A., & Shyamala, L. (2024, 5-7 June 2024). Liver disease Prediction using Ensemble Learning. 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC),
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems, 31.
Raj, H., N, G., Kodipalli, A., & Rao, T. (2024, 28-29 June 2024). Prediction of Chronic Liver Disease Using Machine Learning Algorithms and Interpretation with SHAP Kernels. 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS),
Rewicki, F., Denzler, J., & Niebling, J. (2023). Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series. Applied Sciences, 13(3), 1778. https://www.mdpi.com/2076-3417/13/3/1778
Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
Schapire, R. E. (2013). Explaining AdaBoost. In B. Schölkopf, Z. Luo, & V. Vovk (Eds.), Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik (pp. 37-52). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41136-6_5
Verbakel, J. Y., Steyerberg, E. W., Uno, H., De Cock, B., Wynants, L., Collins, G. S., & Van Calster, B. (2020). ROC curves for clinical prediction models part 1. ROC plots showed no added value above the AUC when evaluating the performance of clinical prediction models. Journal of Clinical Epidemiology, 126, 207-216. https://doi.org/10.1016/j.jclinepi.2020.01.028
Yadav, H. S., & Singhal, R. K. (2023, 3-5 March 2023). Classification and Prediction of Liver Disease Diagnosis Using Machine Learning Algorithms. 2023 2nd International Conference for Innovation in Technology (INOCON),

Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Ümit Yılmaz ^*
0000-0003-4268-8598
Türkiye

Erol Özçekiç
0000-0002-1896-6853
Türkiye

Early Pub Date

August 15, 2025

Publication Date

August 22, 2025

Submission Date

November 27, 2024

Acceptance Date

April 25, 2025

Published in Issue

Year 1970 Volume: 33 Number: 2

DOI

https://doi.org/10.31796/ogummf.1591951

IZ

https://izlik.org/JA97UP77EG

APA

Yılmaz, Ü., & Özçekiç, E. (2025). CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, 33(2), 1882-1892. https://doi.org/10.31796/ogummf.1591951

AMA

1.Yılmaz Ü, Özçekiç E. CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi. 2025;33(2):1882-1892. doi:10.31796/ogummf.1591951

Chicago

Yılmaz, Ümit, and Erol Özçekiç. 2025. “CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi 33 (2): 1882-92. https://doi.org/10.31796/ogummf.1591951.

EndNote

Yılmaz Ü, Özçekiç E (August 1, 2025) CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 33 2 1882–1892.

IEEE

[1]Ü. Yılmaz and E. Özçekiç, “CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES”, Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, vol. 33, no. 2, pp. 1882–1892, Aug. 2025, doi: 10.31796/ogummf.1591951.

ISNAD

Yılmaz, Ümit - Özçekiç, Erol. “CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 33/2 (August 1, 2025): 1882-1892. https://doi.org/10.31796/ogummf.1591951.

JAMA

1.Yılmaz Ü, Özçekiç E. CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi. 2025;33:1882–1892.

MLA

Yılmaz, Ümit, and Erol Özçekiç. “CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, vol. 33, no. 2, Aug. 2025, pp. 1882-9, doi:10.31796/ogummf.1591951.

Vancouver

1.Ümit Yılmaz, Erol Özçekiç. CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi. 2025 Aug. 1;33(2):1882-9. doi:10.31796/ogummf.1591951

CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES

Abstract

Keywords

BOOSTING MAKİNE ÖĞRENME YAKLAŞIMLARI İLE KARACİĞER HASTALIKLARININ SINIFLANDIRILMASI

Öz

Anahtar Kelimeler

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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