The list of parameters of the modified data set for machine learning (<i>n</i> = 162).

The list of parameters of the modified data set for machine learning (<i>n</i> = 162).

<p>The list of parameters of the modified data set for machine learning (<i>n</i> = 162).</p>

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Bibliografski detalji
Glavni autor:	Uğur Ejder (22683228) (author)
Daljnji autori:	Pınar Uskaner Hepsağ (22683231) (author)
Izdano:	2025
Teme:	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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Slični predmeti

The statistical description of the original data set of the patients (<i>n</i> = 162).
od: Uğur Ejder (22683228)
Izdano: (2025)

Top-performing model configuration and performance by algorithm type.
od: Uğur Ejder (22683228)
Izdano: (2025)

Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).
od: Uğur Ejder (22683228)
Izdano: (2025)

Confusion matrix of the ABC–LR–RF hybrid model for IVF outcome prediction.
od: Uğur Ejder (22683228)
Izdano: (2025)

ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction.
od: Uğur Ejder (22683228)
Izdano: (2025)

The most effective active substances for subfertility treatment.
od: Uğur Ejder (22683228)
Izdano: (2025)

The comparison of the accuracy score of the benchmark and the proposed models.
od: Uğur Ejder (22683228)
Izdano: (2025)

The prediction accuracy of simple versus hybrid models.
od: Uğur Ejder (22683228)
Izdano: (2025)

The accuracy score change matrix following model development.
od: Uğur Ejder (22683228)
Izdano: (2025)

Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction.
od: Uğur Ejder (22683228)
Izdano: (2025)

Flow diagram of the proposed model.
od: Uğur Ejder (22683228)
Izdano: (2025)

Flow diagram of the conversion of drugs into active substances.
od: Uğur Ejder (22683228)
Izdano: (2025)

Hospitals by region in Brazil, with a list of hospitals.
od: Roberta Moreira Wichmann (14259316)
Izdano: (2025)

List of variables used to predict hospitalization mortality in the federated and local learning approaches.
od: Roberta Moreira Wichmann (14259316)
Izdano: (2025)

Comparison of predictive performance between federated and local learning models.
od: Roberta Moreira Wichmann (14259316)
Izdano: (2025)

Schematic visualization of the federated learning approaches.
od: Roberta Moreira Wichmann (14259316)
Izdano: (2025)

Regional analysis of socioeconomic indicators and federated learning performance gain.
od: Roberta Moreira Wichmann (14259316)
Izdano: (2025)

Raw performance metrics for the Random Forest (RF) model.
od: Roberta Moreira Wichmann (14259316)
Izdano: (2025)

Raw performance metrics for the MLP model.
od: Roberta Moreira Wichmann (14259316)
Izdano: (2025)

Raw performance metrics for the Logistic Regression (LR) model.
od: Roberta Moreira Wichmann (14259316)
Izdano: (2025)

Evolution of the hyperparameter <i>t</i> in relation to the LR and MLP models.
od: Roberta Moreira Wichmann (14259316)
Izdano: (2025)

Data for "Long-term surveillance of a woodland salamander community with a review of long-term field studies in plethodontids"
od: Richard Lehtinen (12908108)
Izdano: (2025)

Category-level summary of genes with potentially distinct functions between Udai21 and Koshihikari.
od: Hiroki Ikeda (510280)
Izdano: (2025)

Statistics for Udai21 pseudomolecule sequences.
od: Hiroki Ikeda (510280)
Izdano: (2025)

Genome assembly and predicted gene BUSCO scores.
od: Hiroki Ikeda (510280)
Izdano: (2025)

Genome assembly statistics for Udai21.
od: Hiroki Ikeda (510280)
Izdano: (2025)

Classification of Udai21 and the World Rice Core Collection based on organelle genome data.
od: Hiroki Ikeda (510280)
Izdano: (2025)

Classification of Udai21 and the World Rice Core Collection based on whole genome sequencing data.
od: Hiroki Ikeda (510280)
Izdano: (2025)

Rice chromosome SNP density for five rice varieties.
od: Hiroki Ikeda (510280)
Izdano: (2025)

Principal component analysis of Udai21 and the World Rice Core Collection.
od: Hiroki Ikeda (510280)
Izdano: (2025)

Classification of Udai21 and the World Rice Core Collection based on ddRAD-Seq data.
od: Hiroki Ikeda (510280)
Izdano: (2025)

Genes that functions were potentially distincted between Udai21 and Koshihikari.
od: Hiroki Ikeda (510280)
Izdano: (2025)

Genes with high and moderate impact variants between Udai21 and Koshihikari.
od: Hiroki Ikeda (510280)
Izdano: (2025)

Impact damage assessment of sandwich composite materials using non-destructive techniques
od: Tendai Chipanga (22571069)
Izdano: (2025)

DATASET: Data-Driven Clinical Decision Trees and Generative Topographic Mapping to Guide Stroke Rehabilitation Program Allocation
od: Dimitrios-Sokratis Komaris (10266358)
Izdano: (2025)

Resource and Environmental Carrying Capacity Assessment and Forecast Dataset and Code for Xuzhou City, China (2011–2023)
od: 楠姜 (21426950)
Izdano: (2025)

Comparison of hierarchical classifier performance among four machine learning models for the early-loss, intermediate-loss, and late/no-loss groups.
od: Raiki Yoshimura (22676573)
Izdano: (2025)

Comparison of multi-class classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.
od: Raiki Yoshimura (22676573)
Izdano: (2025)

Comparison of binary classifier performance among four machine learning models for early-loss, intermediate-loss, and late/no-loss groups.
od: Raiki Yoshimura (22676573)
Izdano: (2025)

Comparison of performance of four machine learning models for long-term graft loss prediction.
od: Raiki Yoshimura (22676573)
Izdano: (2025)

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