A stacked learning framework for accurate classification of polycystic ovary syndrome with advanced data balancing and feature selection techniques.
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| Title: | A stacked learning framework for accurate classification of polycystic ovary syndrome with advanced data balancing and feature selection techniques. |
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| Authors: | Emara HM; Department of Electronics and Electrical Communications Engineering, Ministry of Higher Education Pyramids Higher Institute (PHI) for Engineering and Technology, 6th of October City, Egypt., El-Shafai W; Automated Systems and Soft Computing Lab (ASSCL), Computer Science Department, Prince Sultan University, Riyadh, Saudi Arabia., Soliman NF; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia., Algarni AD; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia., Alkanhel R; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia., Abd El-Samie FE; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. |
| Source: | Frontiers in physiology [Front Physiol] 2025 May 06; Vol. 16, pp. 1435036. Date of Electronic Publication: 2025 May 06 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101549006 Publication Model: eCollection Cited Medium: Print ISSN: 1664-042X (Print) Linking ISSN: 1664042X NLM ISO Abbreviation: Front Physiol Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1664-042X |
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| DOI: | 10.3389/fphys.2025.1435036 |
