Interpretable XGBoost model identifies idiopathic central precocious puberty in girls using four clinical and imaging features by Lu Tian, Yan Zeng, Helin Zheng, Jinhua Cai

“…The least absolute shrinkage and selection operator (LASSO) method was used to select essential characteristic parameters associated with ICPP and were used to construct logistic regression (LR) and five machine learning (ML) models, including support vector machine (SVM), Gaussian naive bayes (GaussianNB), extreme gradient boosting (XGBoost), random forest (RF), and k- nearest neighbor algorithm (kNN). …”
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Multi‐sequence MRI‐based clinical‐radiomics models for the preoperative prediction of microsatellite instability‐high status in endometrial cancer by Zhuang Li, Yi Su, Yongbin Cui, Yong Yin, Zhenjiang Li

“…Clinical, radiomics, and clinical‐radiomics models were developed in the training set using logistic regression (LR), random forest (RF), and support vector machine (SVM). …”
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Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnex... by Lu Liu, Wenjun Cai, Feibo Zheng, Hongyan Tian, Yanping Li, Ting Wang, Xiaonan Chen, Wenjing Zhu

“…Results The FCN ResNet101 demonstrated the highest segmentation performance for adnexal masses (Dice similarity coefficient: 89.1%). Support vector machine achieved the best AUC (0.961, 95% CI: 0.925–0.996). …”
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The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval by Yucheng Gao, Lixia Ma, Zhongqi Zhang, Xianzhang Pan, Ziran Yuan, Changkun Wang, Dongsheng Yu

“…SOM and PSD were then retrieved using combinations of ten spectral preprocessing methods (raw reflectance, Savitzky–Golay filter (SG), first derivative (D1), second derivative (D2), standard normal variate (SNV), multiplicative scatter correction (MSC), SG + D1, SG + D2, SG + SNV, and SG + MSC), one sensitive wavelength selection method, and three retrieval algorithms (partial least squares regression (PLSR), support vector machine (SVM), and convolutional neural networks (CNNs)). …”
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Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic rhinosinusitis with nasal polyps by Zhaonan Xu, Qing Hao, Bingrui Yan, Qiuying Li, Xuan Kan, Qin Wu, Hongtian Yi, Xianji Shen, Lingmei Qu, Peng Wang, Yanan Sun

“…The least absolute shrinkage and selection operator (LASSO) regression model and multivariate support vector machine recursive feature elimination (mSVM-RFE) were used to identify potential biomarkers, which were validated using the real time quantitative polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC). …”
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Blood pressure abnormality detection and interpretation utilizing explainable artificial intelligence by Hedayetul Islam, Md. Sadiq Iqbal, Muhammad Minoar Hossain

“…We have used several ML algorithms (extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), decision tree (DT), and logistic regression (LR)) to predict blood pressure abnormality based on patient's data. …”
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Machine learning-based radiomics for differentiating lung cancer subtypes in brain metastases using CE-T1WI by Xueming Xia, Wei Du, Qiheng Gou

“…In the training dataset, the top-performing classifiers were the XGBoost, LightGBM, support vector machine (SVM) and random forest models, which achieved AUC of 0.963, 0.881, 0.876 and 0.855, respectively, with 5-fold cross-validation. …”
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Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025 by Kelemua Aschale Yeneakal, Gizaw Hailiye Teferi, Temesgen T. Mihret, Abraham Keffale Mengistu, Sefefe Birhanu Tizie, Maru Meseret Tadele

“…Seven machine learning algorithms: support vector machine, random forest, decision tree, logistic regression, gradient boosting, K-nearest neighbors, and artificial neural network were trained. …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…We compared 10 ML models based on radiomics features: support vector machine (SVM), logistic regression (LR), random forest, extra trees (ET), naïve Bayes (NB), k-nearest neighbor (KNN), multilayer perceptron (MLP), gradient boosting ML (GBM), light GBM (LGBM), and adaptive boost (AB). …”
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Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights by Muhammad Hassan, Khabat Khosravi, Travis J. Esau, Gurjit S. Randhawa, Aitazaz A. Farooque, Seyyed Ebrahim Hashemi Garmdareh, Yulin Hu, Nauman Yaqoob, Asad T. Jappa

“…These model were benchmarked against a support vector regression (SVR) model. The dataset comprised 401 samples from potato fields in Prince Edward Island (PEI) and 122 samples from New Brunswick (NB), including measurements of N2O and H2O and related input variables such as soil moisture (SM), temperature ST, electrical conductivity (EC), wind speed, solar radiation, relative humidity, precipitation, air temperature (AT), dew point, vapor pressure deficit, and reference evapotranspiration. …”
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Advancing Credit Rating Prediction: The Role of Machine Learning in Corporate Credit Rating Assessment by Nazário Augusto de Oliveira, Leonardo Fernando Cruz Basso

“…The study evaluated multiple ML models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting (GB), and Neural Networks, using rigorous data pre-processing, feature selection, and validation techniques. …”
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Analysis of corn price forecast in China based on Lasso-XGBoost-SHAP by Wenming Cheng, Fangyuan Li

“…Results demonstrate that the Lasso-XGBoost model outperforms traditional linear models (LM) and other algorithms, including SVM (Support Vector Machine) and MLP (Multilayer Perceptron), with root mean squared error (RMSE) of 0.094, coefficient of determination (R2) of 0.973, mean absolute error (MAE) of 0.072, representing a 7.84% reduction in RMSE compared to standalone XGBoost. …”
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Diabetic Retinopathy Detection Using DL-Based Feature Extraction and a Hybrid Attention-Based Stacking Ensemble by Sanjana Rajeshwar, Shreya Thaplyal, Anbarasi M., Siva Shanmugam G.

“…Classification employs a decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), and a modified convolutional neural network (CNN) with a spatial attention layer. …”
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Exploring the capabilities of hyperspectral remote sensing for soil texture evaluation by Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker, Reza Alimardani, Pouya Bohlol

“…Additionally, we compared the performance of random forest (RF) algorithms with partial least squares regression (PLSR), multiple linear regression (MLR), support vector machine regression (SVR), decision trees (DTs), and multilayer perceptron (MLP) neural networks, addressing the effects of feature selection and irregular soil data on the modeling procedure. …”
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Improving the accuracy of remotely sensed TSS and turbidity using quality enhanced water reflectance by a statistical resampling technique by Kunwar Abhishek Singh, Dongryeol Ryu, Meenakshi Arora, Manoj Kumar Tiwari, Bhabagrahi Sahoo

“…The resampled spectral data and in-situ TSS and turbidity measurements were used to train four ML models: Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR). …”
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Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study by Jorge Marques Silva, Susana M. Vieira, Duarte Valério, João C. C. Henriques, Paul D. Sclavounos

“…This work intends to exploit the short‐term wave forecasting potential on an oscillating water column equipped with the innovative biradial turbine. A Least Squares Support Vector Machine (LS‐SVM) algorithm was developed to predict the air chamber pressure and compare it to the real signal. …”
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Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models by Hongxia Hao, Jiemin Chen, Yifei Yan, Yifei Yan, Qi Zhang, Qi Zhang, Zhilu Zhou, Wentao Xia

“…Four different machine learning algorithms, namely, support vector regression (SVR), Bayesian ridge (BYR), random forest regression (RFG), and extreme gradient boosting (XGBoost), were used to predict best corrected visual acuity (BCVA) values. …”
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Graph convolution network for fraud detection in bitcoin transactions by Ahmad Asiri, K. Somasundaram

“…We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of Graph Neural Networks, which is called Graph Convolution Network (GCN). …”
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Prediction and Mapping of Soil Total Nitrogen Using GF-5 Image Based on Machine Learning Optimization Modeling by LIU Liqi, WEI Guangyuan, ZHOU Ping

“…Three machine learning algorithms were introduced: Partial least squares regression (PLSR), backpropagation neural network (BPNN), and support vector machine (SVM) driven by a polynomial kernel function (Poly). …”
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Remote sensing inversion of nitrogen content in silage maize plants based on feature selection by Kejing Cheng, Kejing Cheng, Jixuan Yan, Jixuan Yan, Guang Li, Guang Li, Weiwei Ma, Weiwei Ma, Zichen Guo, Zichen Guo, Wenning Wang, Wenning Wang, Haolin Li, Qihong Da, Qihong Da, Xuchun Li, Xuchun Li, Yadong Yao, Yadong Yao

“…This study employs multispectral remote sensing images, combined with field-measured nitrogen content, to develop canopy nitrogen content inversion models for maize using three algorithms: backpropagation neural network (BP), support vector machine (SVM), and partial least squares regression (PLSR). …”
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