NOMINAL (KATEGORIYALI) BELGILARNI SAMARALI SARALASH MUAMMOLARI VA ZAMONAVIY ALGORITMIK YONDASHUVLAR
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Аннотация:
Ushbu tezisda tibbiy ma’lumotlarda keng uchraydigan nominal belgilarni saralash masalalari va zamonaviy algoritmik yondashuvlar tadqiq qilinadi. Raqamli transformatsiya jarayonida tibbiy sohada to‘planayotgan katta hajmdagi yuqori o‘lchamli ma’lumotlarni qayta ishlash, keraksiz belgilarni ajratib tashlash va diagnostik modelarning aniqligi, tezligi va izohlanishini oshirish masalalari yoritiladi. Nominal belgilarni kodlash (masalan, one-hot encoding) va ularni tanlash (feature selection) uchun turli usullar – statistik (o‘zaro axborot), modelga asoslangan (Lasso, RFE) va izohlash vositalari (SHAP, LIME) tahli l qilinadi. Yurak kasalliklarini tashxislash misolida nominal belgilarni saralashning amaliy bosqichlari ko‘rsatilgan. Xulosa qilib, nominal belgilarni to‘g‘ri tanlash va izohlash tibbiy sun’iy intellekt tizimlarining samaradorligi, ishonchliligi va klinik qarorlar sifatini oshirishda muhim ahamiyatga ega ekanligi ta’kidlanadi.
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Библиографические ссылки:
Guyon, I., & Elisseeff, A. (2003). "An Introduction to Variable and Feature Selection".Journal of Machine Learning Research, 3, 1157-1182.
Saeys, Y., Inza, I., & Larrañaga, P. (2007). "A review of feature selection techniques in bioinformatics".Bioinformatics, 23(19), 2507-2517.
Liu, H., & Motoda, H. (Eds.). (2007). "Computational Methods of Feature Selection".Chapman & Hall/CRC.
Bishop, C. M. (2006). "Pattern Recognition and Machine Learning".Springer.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction".Springer (2nd ed.).
Géron, A. (2019). "Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow".O'Reilly Media (2nd ed.).
Molnar, C. (2022). "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable" https://christophm.github.io/interpretable-ml-book/
Rudin, C. (2019). "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead".Nature Machine Intelligence, 1(5), 206-215.
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). "Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission".Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Jiang, F., et al. (2017). "Artificial intelligence in healthcare: past, present and future".Stroke and Vascular Neurology, 2(4).
Rajkomar, A., Dean, J., & Kohane, I. (2019). "Machine Learning in Medicine". The New England Journal of Medicine, 380 (14), 1347-1358.
Jurnallar: Journal of Biomedical Informatics, Artificial Intelligence in Medicine, IEEE Journal of Biomedical and Health Informatics, Nature Machine Intelligence.
Konferensiyalar:Conference on Health, Inference, and Learning (CHIL), Machine Learning for Healthcare (MLHC), AMIA Annual Symposium.