الدكتور خالد صبارنة يشارك في نشر دراسة بعنوان " دعم الخصوبة في متلازمة تكيس المبايض باستخدام التعلم الآلي "

ملخص البحث

Polycystic ovary syndrome (PCOS/PCOD) is a problem in which a woman’s hormones are out of balance PCOS increase serious complications among females this is why it is called polycystic ovary syndrome. PCOS is an alarming endocrinopathy. Undiagnosed PCOS can lead to infertility. One in every 5–6 female is facing serious complications regarding infertility and irregularity in their menstrual cycles this endocrine disorder affects females under 18–44 age. Globally it affects 5–15% of females. Early detection of PCOS can help to reduce complications. Thus, in order to reduce difficulties, an accurate and timely PCOS screening system is crucial. Machine Learning (ML), with its capacity to extract features, performs exceptionally well in assessment, patterns and analysis techniques. As a result, a lot of research has been done to use ML to detect PCOS. The techniques of PCOS involves the application of several machine learning techniques, including Convolutional Neural Network, Support Vector Machine, K-Nearest-Neighbors, Random Forest, Logistic Regression, Decision Tree, Naive Bayes, etc. The hybrid model demonstrates an accuracy rate of 90.01% in detecting PCOS at its early stages. These findings highlight the potential economic benefits of using advanced ML models in medical diagnostics.

كيفية الاستشهاد للبحث:

Xavier Suresh, M., Ganeshan, R., Babar, A., Shaqadan, A., & Sabarna, K. (2026). Fertility Support of PCOS with Machine Learning. In Business Resilience and Business Innovation for Sustainability: The Double-Edged Role of Artificial Intelligence and Other Disruptive Technologies (pp. 2991-3004). Cham: Springer Nature Switzerland.

رابط البحث

https://link.springer.com/chapter/10.1007/978-3-031-87584-7_219