Comparison of decision tree and naive bayes methods in glioma classification based on clinical and molecular factors
DOI:
https://doi.org/10.35335/mandiri.v13i4.389Keywords:
Clinical Factors , Decision Tree Classifier , Glioma Classification , Molecular Factors , Naive Bayes ClassifierAbstract
This study compares the performance of Decision Tree and Naive Bayes classifiers in classifying gliomas based on clinical and molecular factors. The dataset consists of 839 patient records with features including Grade, Gender, Age, Race, and gene mutation status. The evaluation showed that the Decision Tree classifier achieved 98% accuracy on the training data and 76% on the test data, while the Naive Bayes classifier obtained 74% and 71% accuracy, respectively. Both models demonstrated strong predictive ability, with feature importance analysis highlighting the IDH1 gene mutation as a significant factor in glioma classification. This study aims to identify the most effective method for supporting clinical decision-making in glioma diagnosis. It contributes to the development of medical decision support systems and provides insight into the application of machine learning models, particularly in utilizing molecular markers such as IDH1.
References
Abrori, S., & Fatah, Z. (2025). Implementasi Metode Decission Tree Dalam Mengklasifikasi Depresi Menggunakan Rapidminer. 5(2), 123–132.
Arya Darmawan, M. B., Dewanta, F., & Astuti, S. (2023). Analisis Perbandingan Algoritma Decision Tree, Random Forest, dan Naïve Bayes untuk Prediksi Banjir di Desa Dayeuhkolot. TELKA - Telekomunikasi Elektronika Komputasi Dan Kontrol, 9(1), 52–61. https://doi.org/10.15575/telka.v9n1.52-61
Barthel, L., Hadamitzky, M., Dammann, P., Schedlowski, M., Sure, U., Thakur, B. K., & Hetze, S. (2022). Glioma: molecular signature and crossroads with tumor microenvironment. Cancer and Metastasis Reviews, 1–23.
Becker, A. P., Sells, B. E., Haque, S. J., & Chakravarti, A. (2021). Tumor Heterogeneity in Glioblastomas: From Light Microscopy to Molecular Pathology. In Cancers (Vol. 13, Issue 4). https://doi.org/10.3390/cancers13040761
Buntine, W. (2020). Learning classification trees. In Artificial Intelligence frontiers in statistics (pp. 182–201). Chapman and Hall/CRC.
Chen, H., Hu, S., Hua, R., & Zhao, X. (2021). Improved naive Bayes classification algorithm for traffic risk management. EURASIP Journal on Advances in Signal Processing, 2021(1), 30.
Jayatilake, S. M. D. A. C., & Ganegoda, G. U. (2021). Involvement of Machine Learning Tools in Healthcare Decision Making. Journal of Healthcare Engineering, 2021(1), 6679512. https://doi.org/https://doi.org/10.1155/2021/6679512
Jogo, M. M. S., Biddinika, M. K., & Fadlil, A. (2023). Klasifikasi Penyakit Diabetes dengan Algoritma Decision Tree dan Naïve Bayes. RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) Vol., 6(2), 113–118.
Kopitar, L., Kocbek, P., Cilar, L., Sheikh, A., & Stiglic, G. (2020). Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Scientific Reports, 10(1), 1–12. https://doi.org/10.1038/s41598-020-68771-z
Maulana, R., Narasati, R., Herdiana, R., Hamonangan, R., & Anwar, S. (2024). Komparasi Algoritma Decision Tree Dan Naive Bayes Dalam Klasifikasi Penyakit Diabetes. JATI (Jurnal Mahasiswa Teknik Informatika), 7(6), 3865–3870. https://doi.org/10.36040/jati.v7i6.8265
Meizoso, J. P., Moore, H. B., & Moore, E. E. (2021). Fibrinolysis Shutdown in COVID-19: Clinical Manifestations, Molecular Mechanisms, and Therapeutic Implications. Journal of the American College of Surgeons, 232(6), 995–1003. https://doi.org/10.1016/j.jamcollsurg.2021.02.019
Mirbabaie, M., Stieglitz, S., & Frick, N. R. J. (2021). Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. In Health and Technology (Vol. 11, Issue 4). Springer Berlin Heidelberg. https://doi.org/10.1007/s12553-021-00555-5
Nikita, E., & Nikitas, P. (2020). Data mining and decision trees. In Statistics and probability in forensic anthropology (pp. 87–105). Elsevier.
Priyanka, & Kumar, D. (2020). Decision tree classifier: a detailed survey. International Journal of Information and Decision Sciences, 12(3), 246–269.
Rayhan, Y., & Setyohadi, D. B. (2021). Classification of grape leaf disease using convolutional neural network (CNN) with pre-trained model VGG16. 2021 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), 1–5.
Reddy, V. S. K., Meghana, P., Reddy, N. V. S., & Rao, B. A. (2022). Prediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers. Journal of Physics: Conference Series, 2161(1). https://doi.org/10.1088/1742-6596/2161/1/012015
Ren, Q., Zhang, H., Zhang, D., Zhao, X., Yan, L., Rui, J., Zeng, F., & Zhu, X. (2022). A framework of active learning and semi-supervised learning for lithology identification based on improved naive Bayes. Expert Systems with Applications, 202, 117278.
Saarela, M., & Jauhiainen, S. (2021). Comparison of feature importance measures as explanations for classification models. SN Applied Sciences, 3(2), 272.
Saturi, S. (2023). Review on Machine Learning Techniques for Medical Data Classification and Disease Diagnosis. Regenerative Engineering and Translational Medicine, 9(2), 141–164. https://doi.org/10.1007/s40883-022-00273-y
Scornet, E. (2023). Trees, forests, and impurity-based variable importance in regression. Annales de l’Institut Henri Poincare (B) Probabilites et Statistiques, 59(1), 21–52.
Suresh, A., Udendhran, R., & Balamurgan, M. (2020). Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers. Soft Computing, 24(11), 7947–7953.
Syahputri, C. N., & Hasibuan, M. S. (2024). OPTIMASI KLASIFIKASI DECISION TREE DENGAN TEKNIK PRUNING UNTUK MENGURANGI OVERFITTING. JSiI (Jurnal Sistem Informasi), 11(2), 87–96. https://doi.org/10.30656/jsii.v11i2.9161
Tasci, E., Camphausen, K., Krauze, A., & Zhuge, Y. (2022). Glioma Grading Clinical and Mutation Features [Dataset]. https://doi.org/10.24432/C5R62J
Tharwat, A. (2018). Classification assessment methods. Applied Computing and Informatics, 17(1), 168–192. https://doi.org/10.1016/j.aci.2018.08.003
Thomas, T., P. Vijayaraghavan, A., Emmanuel, S., Thomas, T., P. Vijayaraghavan, A., & Emmanuel, S. (2020). Applications of decision trees. Machine Learning Approaches in Cyber Security Analytics, 157–184.
Vellido, A. (2020). The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Computing and Applications, 32(24), 18069–18083.
Wang, Y., Zhang, Y., Lu, Y., & Yu, X. (2020). A Comparative Assessment of Credit Risk Model Based on Machine Learning——a case study of bank loan data. Procedia Computer Science, 174, 141–149.
Wickramasinghe, I., & Kalutarage, H. (2021). Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation. Soft Computing, 25(3), 2277–2293. https://doi.org/10.1007/s00500-020-05297-6
Widya, H., Surya, N., Atina, V., & Maulindar, J. (2022). Penerapan Algoritme Decision Tree Pada Klasifikasi Penyakit Kanker Paru-Paru.
Yang, L., Fu, B., Li, Y., Liu, Y., Huang, W., Feng, S., Xiao, L., Sun, L., Deng, L., Zheng, X., Ye, F., & Bu, H. (2020). Prediction model of the response to neoadjuvant chemotherapy in breast cancers by a Naive Bayes algorithm. Computer Methods and Programs in Biomedicine, 192. https://doi.org/10.1016/j.cmpb.2020.105458
Zubair, M., Wang, S., & Ali, N. (2021). Advanced Approaches to Breast Cancer Classification and Diagnosis. Frontiers in Pharmacology, 11(February), 1–24. https://doi.org/10.3389/fphar.2020.632079
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ni Wayan Emmy Rosiana Dewi, I Made Suwija Putra, Erwinsyah Simanungkalit, Franky Gerald Cliford Manoppo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
