The performance of a machine learning model in predicting accelerometer-derived walking speed by Aleksej Logacjov, Tonje Pedersen Ludvigsen, Kerstin Bach, Atle Kongsvold, Mats Flaaten, Tom Ivar Lund Nilsen, Paul Jarle Mork

“…The video recordings were labelled and used as ground truth for training an eXtreme Gradient Boosting (XGBoost) machine learning classifier. …”
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Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study by Huanhuan Ren, Haojie Song, Shaoguo Cui, Hua Xiong, Bangyuan Long, Yongmei Li

“…A clinical model was developed using eXtreme Gradient Boosting, an NCCT-based imaging model was created using deep learning, and an ensemble model integrated both models. …”
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Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates by Siham Acharki, Ali Raza, Dinesh Kumar Vishwakarma, Mina Amharref, Abdes Samed Bernoussi, Sudhir Kumar Singh, Nadhir Al-Ansari, Ahmed Z. Dewidar, Ahmed A. Al-Othman, Mohamed A. Mattar

“…The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. …”
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Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction by Yu-Hang Wang, Chang-Ping Li, Jing-Xian Wang, Zhuang Cui, Yu Zhou, An-Ran Jing, Miao-Miao Liang, Yin Liu, Jing Gao

“…Subsequently, Lasso–logistic, random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to establish prediction models based on the training set. …”
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Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV h... by Anqi He, Zhanghua Xu, Yifan Li, Bin Li, Xuying Huang, Huafeng Zhang, Xiaoyu Guo, Zenglu Li

“…We analyzed the impact of on-year and off-year phenological characteristics on the accuracy of hazard extraction and developed detection models for P. phyllostachysae hazard levels in on-year and off-year Moso bamboo using Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and one-dimensional Convolutional Neural Network (1D-CNN). …”
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Exploring cement Production's role in GDP using explainable AI and sustainability analysis in Nepal by Ramhari Poudyal, Biplov Paneru, Bishwash Paneru, Tilak Giri, Bibek Paneru, Tim Reynolds, Khem Narayan Poudyal, Mohan B. Dangi

“…Utilizing regression models like Extra Trees (Extremely Randomized Trees) Regressor, CatBoost (Categorial Boosting) Regressor, and XGBoost (eXtreme Gradient Boosting) Regressor, Random Forest and Ensemble of Sparse Embedded Trees (SET) machine learning is used to examine the demand, supply, and Gross Domestic Product (GDP) performance of cement manufacturing in India which shares a common cement related infrastructure to Nepal. …”
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ConvXGB: A novel deep learning model to predict recurrence risk of early-stage cervical cancer following surgery using multiparametric MRI images by Ji Wu, Jian Li, Bo Huang, Sunbin Dong, Luyang Wu, Xiping Shen, Zhigang Zheng

“…We designed a novel deep learning model called “ConvXGB” for predicting recurrence risk by combining the convolutional neural network (CNN) and eXtreme Gradient Boost (XGBoost). The predictive performance of the ConvXGB model was evaluated using time-dependent area under curve (AUC), compared with the deep learning radio-clinical model, clinical model, conventional radiomics nomogram and an existing histology-specific tool. …”
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Development and validation of machine learning models for MASLD: based on multiple potential screening indicators by Hao Chen, Jingjing Zhang, Xueqin Chen, Ling Luo, Wenjiao Dong, Yongjie Wang, Jiyu Zhou, Canjin Chen, Wenhao Wang, Wenbin Zhang, Zhiyi Zhang, Yongguang Cai, Danli Kong, Yuanlin Ding

“…Subsequently, the partial dependence plot(PDP) method and SHapley Additive exPlanations (SHAP) were utilized to explain the roles of important variables in the model to filter out the optimal indicators for constructing the MASLD risk model.ResultsRanking the feature importance of the Random Forest (RF) model and eXtreme Gradient Boosting (XGBoost) model constructed using all variables found that both homeostasis model assessment of insulin resistance (HOMA-IR) and triglyceride glucose-waist circumference (TyG-WC) were the first and second most important variables. …”
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Establishing a radiomics model using contrast-enhanced ultrasound for preoperative prediction of neoplastic gallbladder polyps exceeding 10 mm by Dong Jiang, Yi Qian, Yijun Gu, Ru Wang, Hua Yu, Zhenmeng Wang, Hui Dong, Dongyu Chen, Yan Chen, Haozheng Jiang, Yiran Li

“…This model, derived from machine learning frameworks including Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), k-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost) with fivefold cross-validation, showed AUCs of 0.95 (95% CI: 0.90–0.99) and 0.87 (95% CI: 0.72–1.0) in internal validation. …”
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Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer by Hongyu Wang, Hongyu Wang, Hongyu Wang, Zhiqiang He, Zhiqiang He, Zhiqiang He, Jiayang Xu, Jiayang Xu, Ting Chen, Ting Chen, Ting Chen, Jingtian Huang, Jingtian Huang, Jingtian Huang, Lihong Chen, Lihong Chen, Lihong Chen, Xin Yue, Xin Yue, Xin Yue

“…All six ML models performed well, and in both internal and independent external validations, the eXtreme Gradient Boosting (XGB) model outperformed the other models, with AUC values of 0.87 and 0.80, respectively. …”
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AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism by Lucindah N. Fry-Nartey, Cyril Akafia, Ursula S. Nkonu, Spencer B. Baiden, Ignatus Nunana Dorvi, Kwasi Agyenkwa-Mawuli, Odame Agyapong, Claude Fiifi Hayford, Michael D. Wilson, Whelton A. Miller, Samuel K. Kwofie

“…Predictive models were trained using random forest, adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), k-nearest neighbours (KNN), and decision tree models. …”
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Understanding summertime peroxyacetyl nitrate (PAN) formation and its relation to aerosol pollution: insights from high-resolution measurements and modeling by B. Hu, B. Hu, B. Hu, N. Chen, N. Chen, R. Li, M. Huang, M. Huang, M. Huang, J. Chen, J. Chen, Y. Hong, Y. Hong, L. Xu, L. Xu, X. Fan, X. Fan, M. Li, M. Li, L. Tong, Q. Zheng, Y. Yang

“…The MCM model, with an index of agreement (IOA) value of 0.75, effectively investigates PAN formation, performing better during the clean period (<span class="inline-formula"><i>R</i>²</span>: 0.68; slope <span class="inline-formula"><i>K</i></span>: 0.91) than the haze one (<span class="inline-formula"><i>R</i>²</span>: 0.47; slope <span class="inline-formula"><i>K</i></span>: 0.75). Using eXtreme Gradient Boosting (XGBoost), we identified NH<span class="inline-formula">₃</span>, NO<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M13" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">3</mn><mo>-</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="9pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="a192f22c747584054322d55d69a940ca"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-905-2025-ie00001.svg" width="9pt" height="16pt" src="acp-25-905-2025-ie00001.png"/></svg:svg></span></span>, and PM<span class="inline-formula">_2.5</span> as the primary factors for simulation bias. …”
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Development, validation, and clinical application of a machine learning model for risk stratification and management of cervical cancer screening based on full-genotyping hrHPV tes... by Binhua Dong, Zhen Lu, Tianjie Yang, Junfeng Wang, Yan Zhang, Xunyuan Tuo, Juntao Wang, Shaomei Lin, Hongning Cai, Huan Cheng, Xiaoli Cao, Xinxin Huang, Zheng Zheng, Chong Miao, Yue Wang, Huifeng Xue, Shuxia Xu, Xianhua Liu, Huachun Zou, Pengming Sun

“…Methods: We developed, compared and validated four machine learning models (eXtreme gradient boosting [XGBoost], support vector machine [SVM], random forest [RF], and naïve bayes [NB]) for cervical cancer prediction, using data from a national cervical cancer screening project conducted in 267 healthcare centers in China. …”
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