Evaluating the efficacy of using large language models in preoperative prediction of microvascular invasion in HCC: a multicenter study by Zongren Ding, Jianxing Zeng, Guoxu Fang, Pengfei Guo, Weiping Zhou, Yongyi Zeng

“…The results showed that the AUC of the ChatGPT 4o was 0.755. Machine learning algorithms use Random Forest, Support Vector Machine, Logistic Regression, XGBoost and Decision Tree, the AUC of 5 machine learning algorithms was range from 0.534 to 0.624. …”
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Effective Machine Learning Techniques for Dealing with Poor Credit Data by Dumisani Selby Nkambule, Bhekisipho Twala, Jan Harm Christiaan Pretorius

“…In this paper, an empirical comparison of the robustness of seven machine learning algorithms to credit risk, namely support vector machines (SVMs), naïve base, decision trees (DT), random forest (RF), gradient boosting (GB), K-nearest neighbour (K-NN), and logistic regression (LR), is carried out using the Lending Club credit data from Kaggle. …”
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Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry by Ari Primantara, Udisubakti Ciptomulyono, Berlian Al Kindhi

“…This study investigates the use of machine learning to predict weight deviations in the Urea Bagging Unit at PT Petrokimia Gresik. Four algorithms were used: an Artificial Neural Network (ANN), Random Forest Regression (RFR), Linear Regression (LR), and Support Vector Regression (SVR). …”
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Unveiling global flood hotspots: Optimized machine learning techniques for enhanced flood susceptibility modeling by Mahdi Panahi, Khabat Khosravi, Fatemeh Rezaie, Zahra Kalantari, Jeong-A. Lee

“…Flood susceptibility mapping is a cost-effective tool to mitigate and manage the impacts of flood occurrences, but high accuracy in mapping is important to support management strategies. This study assessed the efficiency of three machine learning approaches, including support vector regression (SVR) and its optimized versions through combination with grey wolf optimizer (GWO) and whale optimization algorithm (WOA), in generating accurate flood susceptibility maps at a global scale. …”
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A risk prediction model for poor joint function recovery after ankle fracture surgery based on interpretable machine learning by Congyang Li, Chenggang Wang, Jiru Zhang, Wenjun Zheng, Jing Shi, Li Li, Xuezhi Shi

“…Feature variables were selected using the Boruta algorithm, and five machine learning algorithms (logistic regression, random forest, extreme gradient boosting, support vector machine, and lasso-stacking) were employed to construct models. …”
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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…The model estimates eGFR using three established CKD Epidemiology Collaboration (CKD-EPI) equations incorporating SCr, SCysC, and their combined values. Regression models assess predictive performance, specifically Linear Regression (LR) and Support Vector Regression (SVR). …”
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Predictive modeling for rework detection in sustainable building projects by AbdulLateef Olanrewaju, Kafayat Shobowale

“…Six machine learning models that comprised support vector machine, Adaboost, Logistic regression, a K-nearest neighbour, neural network and random forest classifier were trained to predict the occurrence of reworks in sustainable buildings. …”
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Design and Application of an Energy Management System Based on Artificial Intelligence Technology by Hongye Lin, Xuanying Bai, Chun Li, Shenghan Xu, Haibin Xu, Zne-Jung Lee, Yun Lin, Qunshan Zhou, Jingxun Cai

“…Among the various types of regression algorithms, the mean-square error (<i>MSE</i>) of decision tree regression is 0.36, the <i>MSE</i> of support vector regression (SVR) is 0.09, the <i>MSE</i> of K-nearest neighbor (KNN) regression is 0.57, and the <i>MSE</i> of extreme gradient boosting (XGBoost) regression is 0.32. …”
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Monitoring of vegetation chlorophyll content in photovoltaic areas using UAV-mounted multispectral imaging by Ming Li, Weiyi Wang, Haoran Li, Zekun Yang, Jianjun Li

“…The selected features were then used in three modeling strategies—vegetation index–based, texture feature–based, and fused index–texture–based—employing three conventional machine-learning regressors (partial least squares regression, random forest, support vector machine regression) and three deep-learning regressors (back propagation neural network, convolutional neural network, multilayer perceptron). …”
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Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution by Yichen Wu, Zhihua Zhang, M. James C. Crabbe, Lipon Chandra Das

“…In this study, based on three statistical learning algorithms, such as support vector machine (SVM), random forest regression (RF), and gradient boosting regressor (GBR), we proposed an efficient downscaling approach to produce high spatial resolution precipitation. …”
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Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City by Afsaneh Ghasemi, Jamil Amanollahi

“…After preprocessing and processing data, data mining models including Classification & Regression Tree (CART), Chi-squared Automatic Interaction Detector (CHAID), Induction of Decision Trees (C5) and neural networks Radial Basis Function (RBF), Multi Layer Perceptron (MLP) and Support Vector Machine (SVM) were used in Spss Modeler Ver 20 software. …”
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Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning by Neng Wang, Shuai Tao, Liang Chen

“…We utilized six machine learning algorithms—Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT)—to construct predictive models. …”
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Real-Time Detection and Localization of Force on a Capacitive Elastomeric Sensor Array Using Image Processing and Machine Learning by Peter Werner Egger, Gidugu Lakshmi Srinivas, Mathias Brandstötter

“…Machine learning models such as linear regression, Support Vector Machine, decision tree, and Gaussian Process Regression were evaluated to correlate force with capacitance values. …”
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Frailty in older adults patients: a prospective observational cohort study on subtype identification by Zhikai Yang, Chen Ji, Ting Wang, Wei He, Yuhao Wan, Min Zeng, Di Guo, Lingling Cui, Hua Wang

“…Key variables for predictive modeling were identified through LASSO (least absolute shrinkage and selection operator) regression, SVM–RFE (support vector machine–recursive feature elimination), and random forest techniques. …”
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Prediction method of gas content in deep coal seams based on logging parameters: A case study of the Baijiahai region in the Junggar Basin by Yijie Wen, Shu Tao, Fan Yang, Yi Cui, Qinghe Jing, Jie Guo, Shida Chen, Bin Zhang, Jincheng Ye

“…The Model-Agnostic Meta-Learning (MAML) and Support Vector Regression (SVR) algorithms are among the few suitable for small-sample learning, exhibiting strong adaptability under limited sample conditions. …”
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Transfer Learning Estimation and Transferability of LNC and LMA Across Different Datasets by Yingbo Wang, Mengzhu He, Lin Sun, Yong He, Zengwei Zheng

“…The LNC and LMA estimation performance in transfer models established by partial least squares regression (PLS), support vector regression (SVR), extreme gradient boosting (XGB), and random forest regression (RFR) algorithms across different datasets were employed, in which the RFR transfer models performed good prediction results. …”
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Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China by Mingxue Xiang, Gang Fu, Jianghao Cheng, Tao Ma, Yunqiao Ma, Kai Zheng, Zhaoqi Wang

“…., random forest model, generalized boosting regression model, multiple linear regression model, artificial neural network model, generalized linear regression model, conditional inference tree model, extreme gradient boosting model, support vector machine model, and recursive regression tree) in Xizang grasslands. …”
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Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases by Wanshan Ning, Zhicheng Wang, Ying Gu, Lindan Huang, Shuai Liu, Qun Chen, Yunyun Yang, Guolin Hong

“…We selected logistic regression, random forest, support vector machine, eXtreme Gradient Boosting (XGBoost), and deep neural network to construct models. …”
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Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery by Hongyan Yang, Jixuan Yan, Guang Li, Weiwei Ma, Xiangdong Yao, Jie Li, Qihong Da, Xuchun Li, Kejing Cheng

“…This study utilized UAV (unmanned aerial vehicle) multispectral imagery and field-measured TN data from four key growth stages of silage corn in 2022 at Huari Ranch, Minle County, Hexi region. The support vector machine–recursive feature elimination (SVM-RFE) algorithm was applied to select vegetation indices as model inputs. …”
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Explainable Machine Learning to Predict the Construction Cost of Power Plant Based on Random Forest and Shapley Method by Suha Falih Mahdi Alazawy, Mohammed Ali Ahmed, Saja Hadi Raheem, Hamza Imran, Luís Filipe Almeida Bernardo, Hugo Alexandre Silva Pinto

“…The RF algorithm was contrasted with single-learner machine learning models: Support Vector Regression (SVR) and k-Nearest Neighbors (KNN). …”
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