Flow diagram of the proposed model.

<div><p>Machine learning models are increasingly applied to assisted reproductive technologies (ART), yet most studies rely on conventional algorithms with limited optimization. This proof-of-concept study investigates whether a hybrid Logistic Regression–Artificial Bee Colony (LR–ABC) f...

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第一著者:	Uğur Ejder (22683228) (author)
その他の著者:	Pınar Uskaner Hepsağ (22683231) (author)
出版事項:	2025
主題:	Biotechnology Ecology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified small sample size lr &# 8211 evaluated using 5 assisted reproductive technologies artificial bee colony art ), yet address class imbalance support vector machine pharmaceutical supplement use enhance predictive performance abc hybrids outperformed abc hybrid counterparts local interpretable model abc hybrid model model performance supplement variables producing interpretable dietician support vitro fertilization synthetic minority studies rely sampling technique retrospective dataset regression tree random forest observed improvements nearest neighbors limited optimization influential features individual predictions improving prediction implemented alongside future studies four algorithms folic acid fold cross exploratory rather dietary data conventional algorithms concept study clinically directive binary representation baseline models algorithm models agnostic explanations accuracy ). 21 predictors
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Flow diagram of the proposed model.

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