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

Subjects:

Flow diagram of the proposed model. by Uğur Ejder (22683228)

Subjects:

The comparison of the accuracy score of the benchmark and the proposed models. by Uğur Ejder (22683228)

Subjects:

The prediction accuracy of simple versus hybrid models. by Uğur Ejder (22683228)

Subjects:

The accuracy score change matrix following model development. by Uğur Ejder (22683228)

Subjects:

Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%). by Uğur Ejder (22683228)

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

The most effective active substances for subfertility treatment. by Uğur Ejder (22683228)

Subjects:

The list of parameters of the modified data set for machine learning (<i>n</i> = 162). by Uğur Ejder (22683228)

Subjects:

Flow diagram of the conversion of drugs into active substances. by Uğur Ejder (22683228)

Subjects:

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…This leads to a set of predictive, yet diverse, interactions that <b>(F)</b> we use to define the score weighting their contribution by fitting a LR model and retaining the corresponding <i>β</i> coefficients.…”

The flowchart of the proposed algorithm. by Muhammad Ayyaz Sheikh (18610943)

“…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …”

Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. by Ramsés Noguez Imm (6658637)

ROC curves for the test set of four models. by Meng Cao (105914)

“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.…”

SHAP bar plot. by Meng Cao (105914)

“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.…”

Sample screening flowchart. by Meng Cao (105914)

“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.…”

Descriptive statistics for variables. by Meng Cao (105914)

“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.…”