| dc.contributor.author | Бралатан, Р. А. | uk |
| dc.contributor.author | Жуков, С. О. | uk |
| dc.date.accessioned | 2025-09-12T10:01:54Z | |
| dc.date.available | 2025-09-12T10:01:54Z | |
| dc.date.issued | 2025 | |
| dc.identifier.citation | | uk |
| dc.identifier.uri | https://ir.lib.vntu.edu.ua//handle/123456789/49182 | |
| dc.description.abstract | У цій роботі розглядаються методи байєсівського моделювання для прогнозування ризику розвитку онкологічних захворювань. Основна увага приділяється алгоритмам Gaussian Naive Bayes (GaussianNB) та | uk |
| dc.description.abstract | This paper examines Bayesian modeling methods for predicting the risk of developing cancer. The main focus is on the Gaussian Naive Bayes (GaussianNB) and Bayesian Ridge algorithms, tested on a Kaggle dataset. An analysis of their effectiveness was conducted, and the results were compared to determine the most suitable approach for medical predictions. | en |
| dc.language.iso | uk_UA | uk_UA |
| dc.publisher | ВНТУ | uk |
| dc.relation.ispartof | // Матеріали LIV науково-технічної конференції підрозділів ВНТУ, Вінниця, 24-27 березня 2025 р. | uk |
| dc.relation.uri | https://conferences.vntu.edu.ua/index.php/all-fksa/all-fksa-2025/paper/view/23210 | |
| dc.subject | Байєсівські моделі | uk |
| dc.subject | GaussianNB | uk |
| dc.subject | Bayesian Ridge | uk |
| dc.subject | прогнозування раку | uk |
| dc.subject | машинне навчання | uk |
| dc.subject | Bayesian models | uk |
| dc.subject | GaussianNB | uk |
| dc.subject | Bayesian Ridge | uk |
| dc.subject | cancer prediction | uk |
| dc.subject | machine learning | uk |
| dc.title | Байєсівське моделювання для оцінки ризиків виникнення раку легенів на основі аналізу медичних даних | uk |
| dc.type | Thesis | |
| dc.identifier.udc | 004.67 + 616.2 | |
| dc.relation.references | Rodinkova V., Yuriev S., Mokin V., Kryvopustova M., Shmundiak D., Bortnyk M., Kryzhanovskyi Y., Kurchenko A. Bayesian analysis suggests independent development of sensitization to different fungal allergens. World Allergy Organization Journal. 2024. Vol. 17, no. 5. P. 100908, [ ] : https://doi.org/10.1016/j.waojou.2024.100908 | |
| dc.relation.references | S.P. Bhargav; S. Om Prakash; S. Hariharasudhan; P. Tamilselvi. Impact of PCA on Lung Cancer Dataset Classification: A Comparitive Analysis of Machine Learning Models. Published in International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), 2024, [ ]. : https://ieeexplore.ieee.org/abstract/document/10533485 | |
| dc.relation.references | Mohammad Shafiquzzaman Bhuiyan, Imranul Kabir Chowdhury, Mahfuz Haider, Afjal Hossain Jisan, Rasel Mahmud Jewel, Rumana Shahid, Mst Zannatun Ferdus, & Siddiqua, C. U. (2024). Advancements in Early Detection of Lung Cancer in Public Health: A Comprehensive Study Utilizing Machine Learning Algorithms and Predictive Models. Journal of Computer Science and Technology Studies, 6(1), 113-121, [ ]. : https://doi.org/10.32996/jcsts.2024.6.1.12 | |
| dc.relation.references | Zhong, L., Yang, F., Sun, S. et al. Predicting lung cancer survival prognosis based on the conditional survival bayesian network. BMC Med Res Methodol 24, 16 (2024), [ ]. : https://doi.org/10.1186/s12874-023-02043-y | |
| dc.relation.references | Roman Bralatan notebook [ ] : https://www.kaggle.com/code/romantick/notebook4acec5e3be | |
| dc.relation.references | Lung Cancer Risk & Trends Across 25 Countries Dataset. Kaggle. 2023 [ ]. : https://www.kaggle.com/datasets/ankushpanday1/lung-cancer-risk-and-trends-across-25-countries | |