Features selection using the Boruta algorithm. by Nishat Tasnim Thity (21755858)

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Feature selection using Boruta algorithm. by Shayla Naznin (13014015)

“…</p><p>Methods</p><p>Multiple machine learning (ML) algorithms were applied to data from the 2022 Bangladesh Demographic Health Survey, including Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, XGBoost, LightGBM and Neural Networks. …”

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

“…</p><p>Results</p><p>Our study included 2,213 patients, of whom 345 (15.6%) experienced in-hospital mortality. The Boruta algorithm identified 29 significant risk factors, and the top 13 variables were used for developing machine learning models. …”

Types of machine learning algorithms. by Md. Merajul Islam (12646837)

“…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.</p><p>Materials and methods</p><p>This study used the latest nationally representative cross-sectional Bangladesh demographic health survey (BDHS), 2017–18 data. …”

Comparison of the EODA algorithm with existing algorithms in terms of recall. by Sunil Kumar (102321)

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Comparison of the EODA algorithm with existing algorithms in terms of precision. by Sunil Kumar (102321)

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Comparison of the EODA algorithm with existing algorithms in terms of F1-Score. by Sunil Kumar (102321)

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Data Sheet 3_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 2_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 4_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 6_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.docx by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 1_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.pdf by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

The proposed algorithm of the outlier detection method. by Sunil Kumar (102321)

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Receiver operator characteristic (ROC) curves of the machine learning algorithms. by Yue Lv (549862)

Performance evaluation measure of the machine learning algorithm after data balancing with SMOTE and grid search tuning. by Alemu Birara Zemariam (17540938)

“…<p>Performance evaluation measure of the machine learning algorithm after data balancing with SMOTE and grid search tuning.…”

Features identified through LASSO analyses and Boruta. by Lang Gao (12963876)

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ROC curve analysis of machine learning algorithms with balanced data before tuning (A) and after optimized with hyper parameter tuning (B). by Alemu Birara Zemariam (17540938)

“…<p>ROC curve analysis of machine learning algorithms with balanced data before tuning (A) and after optimized with hyper parameter tuning (B).…”

Heuristic approaches for configuring network alignment algorithms. by Giuseppe Agapito (104634)

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Parameters of ML algorithms. by Md. Maeen Molla (20827041)

Table 1_Association between the stress hyperglycemia ratio and all-cause mortality in patients with hemorrhagic stroke: a retrospective analysis based on MIMIC-IV database.docx by Wei Zhu (23646)