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. …”

RFEcv algorithm flowchart. by Xinrui Wang (488330)

Data_Sheet_1_Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.pdf by Mikhail Ya Yadgarov (18543769)

Variables tested in the ML algorithms. by Gilson Yuuji Shimizu (19837946)

“…Data from Beth Israel Deaconess Medical Center (BIDMC), USA, were used for external validation. …”

bppMigration-algorithms-data.tgz by Ziheng Yang (22169779)

“…<p dir="ltr">Population phylogenomics uses sampled genomes to jointly infer population genetic processes (ancestral and contemporary population sizes, historical gene flow) and a phylogenetic tree relating species or populations including species divergence times. …”

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

Subjects:

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. …”

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

Comparison of Proposed Model using Unseen Data. by Saim Chishti (22272188)

“…To effectively analyze more complex medical data, more robust machine learning models have been developed to address various health issues. …”

Comparison of proposed model using unseen data. by Saim Chishti (22272188)

“…To effectively analyze more complex medical data, more robust machine learning models have been developed to address various health issues. …”

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

“…This study focuses on developing an efficient classification framework for species-level tree mapping in the Hauz Khas Urban Forest, New Delhi, India, using EO-1 Hyperion hyperspectral imagery.…”

Update mechanism. by Saad Hammood Mohammed (20623506)

Consistency results of raw data. by Haotong Han (20855036)

“…<div><p>Objective</p><p>An information security evaluation model based on the K-Means Clustering (KMC) +  Decision Tree (DT) algorithm is constructed, aiming to assess its value in evaluating smart city (SC) security. …”

Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability by Okechukwu Paul-Chima Ugwu (21578037)

Flowchart of the artificial bee colony algorithm. by Wei Su (89824)

“…The findings demonstrate the effectiveness of combining an intelligent optimization algorithm with the LSSVM model. This approach is reliable for predicting the porosity in complex formations and performing reservoir evaluations in oil and gas exploration and development.…”

DataSheet1_Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm.docx by Qiyuan Cui (19729288)

“…Objective<p>The aim of this study was to develop and validate a machine learning-based model to predict the development of impaired fasting glucose (IFG) in middle-aged and older elderly people over a 5-year period using data from a cohort study.…”