A Novel Approach Utilizing Bagging, Histogram Gradient Boosting, and Advanced Feature Selection for Predicting the Onset of Cardiovascular Diseases by Norma Latif Fitriyani, Muhammad Syafrudin, Nur Chamidah, Marisa Rifada, Hendri Susilo, Dursun Aydin, Syifa Latif Qolbiyani, Seung Won Lee

“…This research presents a novel prediction model for CVDs utilizing a bagging algorithm that incorporates histogram gradient boosting as the estimator. This study leverages three preprocessed cardiovascular datasets, employing the Local Outlier Factor technique for outlier removal and the information gain method for feature selection. …”
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Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield by Niaz Muhammad Shahani, Muhammad Kamran, Xigui Zheng, Cancan Liu, Xiaowei Guo

“…In this study, four gradient boosting machine learning algorithms, namely, gradient boosted regression (GBR), Catboost, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were developed to predict the UCS in MPa of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan, using four input variables such as wet density (ρw) in g/cm3; moisture in %; dry density (ρd) in g/cm3; and Brazilian tensile strength (BTS) in MPa. …”
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Prediction of Earthquake Death Toll Based on Principal Component Analysis, Improved Whale Optimization Algorithm, and Extreme Gradient Boosting by Chenhui Wang, Xiaotao Zhang, Xiaoshan Wang, Guoping Chang

“…To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). The method first employs PCA to reduce the dimensionality of the influencing factor data, eliminating redundant information and improving modeling efficiency. …”
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hERG toxicity prediction in early drug discovery using extreme gradient boosting and isometric stratified ensemble mapping by Gabriela Falcón-Cano, Aliuska Morales-Helguera, Heather Lambert, Miguel-Ángel Cabrera-Pérez, Christophe Molina

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Investigating the Predictive Performance of Process Data and Result Data in Complex Problem Solving Using the Conditional Gradient Boosting Algorithm by Fatma Nur Aydin, Kubra Atalay Kabasakal, Ismail Dilek

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Integration of LSTM networks with gradient boosting machines (GBM) for assessing heating and cooling load requirements in building energy efficiency by Reenu Batra, Shakti Arora, Mayank Mohan Sharma, Sonu Rana, Kanishka Raheja, Abeer Saber, Mohd Asif Shah

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Forecasting monthly runoff in a glacierized catchment: A comparison of extreme gradient boosting (XGBoost) and deep learning models. by Mohammed Majeed Hameed, Adil Masood, Aadil Hamid, Ahmed Elbeltagi, Siti Fatin Mohd Razali, Ali Salem

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Assessing the risk of traffic accidents in lisbon using a gradient boosting algorithm with a hybrid classification/regression approach by Nuno Alpalhão, Pedro Sarmento, Bruno Jardim, Miguel de Castro Neto

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Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm by Qiyuan Cui, Jianhong Pu, Wei Li, Yun Zheng, Jiaxi Lin, Lu Liu, Peng Xue, Jinzhou Zhu, Mingqing He

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Explainable artificial intelligence visions on incident duration using eXtreme Gradient Boosting and SHapley Additive exPlanations by Khaled Hamad, Emran Alotaibi, Waleed Zeiada, Ghazi Al-Khateeb, Saleh Abu Dabous, Maher Omar, Bharadwaj R.K. Mantha, Mohamed G. Arab, Tarek Merabtene

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Flusion: Integrating multiple data sources for accurate influenza predictions by Evan L. Ray, Yijin Wang, Russell D. Wolfinger, Nicholas G. Reich

“…The gradient boosting models were trained on all three data signals, while the autoregressive model was trained on only data for the target surveillance signal, NHSN admissions; all three models were trained jointly on data for multiple locations. …”
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Comparing 2D and 3D Feature Extraction Methods for Lung Adenocarcinoma Prediction Using CT Scans: A Cross-Cohort Study by Margarida Gouveia, Tânia Mendes, Eduardo M. Rodrigues, Hélder P. Oliveira, Tania Pereira

“…Firstly, radiomic features were classified by Random Forest and eXtreme Gradient Boosting classifiers. Next, a deep learning approach, based on a Residual Neural Network and a Transformer-based architecture, was utilised. …”
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Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women by Peng Wang, Qiang Yin, Kangzhi Ding, Huaichang Zhong, Qundi Jia, Zhasang Xiao, Hai Xiong

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Partner relationships, hopelessness, and health status strongly predict maternal well-being: an approach using light gradient boosting machine by Hikaru Ooba, Jota Maki, Takahiro Tabuchi, Hisashi Masuyama

“…We developed and validated a light Gradient Boosting Machine (lightGBM) model using data from 400 pregnant women in 2020 to identify three factors that predict subjective well-being. …”
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Utilizing Machine Learning and Multi-Station Observations to Investigate the Visibility of Sea Fog in the Beibu Gulf by Qin Huang, Peng Zeng, Xiaowei Guo, Jingjing Lyu

“…It examines the diurnal variations of sea fog occurrence and compares the performance of three machine learning (ML) models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—in predicting visibility associated with sea fog in the Beibu Gulf. …”
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GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets by Ehsan Namjoo, Alison N. O’Connor, Jim Buckley, Conor Ryan

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The nonlinear relationship between built environment and cycling propensity for different travel purposes − based on extreme gradient boosting decision tree by Xiaonan Zhang, Borui Yan, Bo Yao, Jianfeng Xue, Yan Zheng, Chaojun Ren

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Metaheuristic optimization of extreme gradient boosting machine for enhanced prediction of lateral strength of reinforced concrete columns under cyclic loadings by Phu-Anh-Huy Pham, Nhat-Duc Hoang

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Spatial Heterogeneity in the Nonlinear Impact of Built Environment on Commuting Time of Active Users: A Gradient Boosting Regression Tree Approach by Jingxian Wu, Guikong Tang, Huapeng Shen, Soora Rasouli

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Determining Driving Risk Factors from Near-Miss Events in Telematics Data Using Histogram-Based Gradient Boosting Regressors by Shuai Sun, Montserrat Guillen, Ana M. Pérez-Marín, Linglin Ni

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