Patient selection algorithm. حسب Dwi Kisworo Setyowireni (22656144)

Participant selection algorithm. حسب Alex Gough (16734033)

Participant selection algorithm. حسب Alex Gough (16734033)

Feature selection using the Boruta algorithm. حسب Guang Tu (22054865)

الموضوعات:

Variable selection procedure using the Boruta algorithm. حسب Qiudie Liu (22655907)

الموضوعات:

The patient selection procedure. حسب Xiaoyan Yang (259084)

Feature selection results from the Boruta algorithm. حسب Junying Wei (1710550)

الموضوعات:

Diagnoses which were not selected by either algorithm across the three cohorts. حسب Mohammad A. Al-Mamun (6086642)

Data generation algorithm. حسب Aluwani Ramalata (21262977)

Selected baseline characteristics among patients with Alzheimer’s disease by phenotype algorithm in Optum® EHR. حسب Gowtham A. Rao (8261928)

"…<p>Selected baseline characteristics among patients with Alzheimer’s disease by phenotype algorithm in Optum® EHR.…"

Top features identified by the stepwise additive feature selection algorithm. حسب Kylie K. Hornaday (9713558)

Flowchart of the selection process for patients who underwent PD using the REIN – SNDS linkage algorithm. حسب Bruno Legendre (21989445)

"…<p>Flowchart of the selection process for patients who underwent PD using the REIN – SNDS linkage algorithm.…"

Most important features as selected by the Sequential Feature Selection algorithm (where appropriate) and found optimal hyperparameters for the top 3 scoring models. حسب Andres Portocarrero-Bonifaz (21367929)

Proposed genetic algorithm for feature selection. حسب Shirin Dehghan (19837936)

"…Using GA, Random Forest also demonstrated strong performance, achieving an accuracy rate of 87.4%. Genetic Algorithm significantly improved the performance of all classifiers, emphasizing the importance of feature selection. …"

Predictive performance of 9 ML algorithms in training and validation sets for LAT/SEC in NVAF patients. حسب Chaoqun Huang (529718)

Flow chart of the study population selection. حسب Elisa Dal Canto (9582176)

Selected variables to detect potential tolerant patients. حسب Gloria López-Díaz (22806259)

Dataset of 10 patients (few features have been selected for this example). حسب Saim Chishti (22272188)

The final values of the cost function, the selected features number, and the implementation time for the introduced meta-heuristic algorithms. حسب Hamed Khodadadi (21446172)

Flow diagram of the patient selection process. حسب Li Wang (15202)

"…Finally, a unique double-layer stacking model is designed to improve the performance of the algorithm. Seven classical artificial intelligence methods of Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (ADB), Extra Tree (ET), and Gradient Boosting Decision Tree (GBDT) were selected as candidate models for the base model of the first layer of the model, and extreme gradient enhancement (XGBOOST) was selected as the meta-model for the second layer.…"