Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade olive oils and hazeln... by Derick Malavi, Katleen Raes, Sam Van Haute

“…Classification was performed using Partial Least Squares-Discriminant Analysis (PLS-DA) and ML algorithms, including k-Nearest Neighbors (k-NN), Naïve Bayes, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). …”
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Development and Validation of a Radiomics Nomogram Based on Magnetic Resonance Imaging and Clinicoradiological Factors to Predict HCC TACE Refractoriness by Dong Y, Hu J, Meng X, Yang B, Peng C, Zhao W

“…Relevant indices of TACE refractoriness were selected. ML algorithms, including a support vector machine, random forest, logistic regression and adaptive boosting, were used to construct the radiomics, clinical prediction, and combined models. …”
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Prediction of EGFR mutations in non-small cell lung cancer: a nomogram based on 18F-FDG PET and thin-section CT radiomics with machine learning by Jianbo Li, Qin Shi, Yi Yang, Jikui Xie, Qiang Xie, Ming Ni, Xuemei Wang, Xuemei Wang

“…After selecting optimal radiomic features, four machine learning algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were used to develop and validate radiomics models. …”
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The Persistent Threat of Chronic Inflammation on the Mortality Among Cervical Cancer Survivors: A Mendelian Randomization and Machine Learning Analysis Using UK Biobank and Chinese... by Wang J, Chen Z, Guan M, Ma Z, Peng L, Chen J, Fiori PL, Carru C, Capobianco G, Coradduzza D, Zhou L

“…However, neither reverse MR, nor Bayesian colocalization analyses supported shared causal variation. After feature selection with 3 algorithms (LASSO regression, Boruta and Support vector machines), the gradient boosting machine (GBM) model outperformed other models by achieving an area under the curve (AUC) of 0.930 and a Brier score of 0.027 in 1-year overall survival (OS) prediction. …”
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A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes by Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada

“…We evaluated three optimization approaches—Grid Search (GS), Random Search (RS), and Bayesian Search (BS)—across three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). …”
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Detection of Agricultural Terraces Platforms Using Machine Learning from Orthophotos and LiDAR-Based Digital Terrain Model: A Case Study in Roya Valley of Southeast France by Michael Vincent Tubog, Karine Emsellem, Stephane Bouissou

“…This study aimed to develop a semi-automatic method for detecting and mapping terraced areas using LiDAR and orthophoto data from French repositories, processed with GIS and analyzed through a Support Vector Machine (SVM) classification algorithm. …”
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Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review by Farkhondeh Asadi, Milad Rahimi, Nahid Ramezanghorbani, Sohrab Almasi

“…Notably, most publications emerged after 2021. Commonly used algorithms for survival prediction included random forest, support vector machines, logistic regression, XGBoost, and various deep learning models. …”
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Application research and effectiveness evaluation mechanism of hybrid intelligent algorithm integrating cognitive computing and deep learning for dynamically adjusting employee per... by Zhenlin Luo, Kebin Lu

“…Simultaneously, hybrid intelligent algorithms outperform support vector machines (SVM) in terms of response speed, with a 61.9% reduction in response time compared to SVM, highlighting their advantages in processing large-scale datasets. …”
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Association of sarcopenia with all-cause and cause-specific mortality in cancer patients: development and validation of a 3-year and 5-year survival prediction model by Feng Cui, Xiangji Dang, Daiyun Peng, Yuanhua She, Yubin Wang, Ruifeng Yang, Zhiyao Han, Yan Liu, Hanteng Yang

“…Subsequently, we employed multivariable Cox regression models to investigate the association between sarcopenia and all-cause and cancer-specific mortality in cancer patients. We developed five machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), LightGBM, and XGBoost, to predict 3-year and 5-year survival rates and to perform risk stratification. …”
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A risk prediction model for gastric cancer based on endoscopic atrophy classification by Yadi Lan, Weijia Sun, Shen Zhong, Qianqian Xu, Yining Xue, Zhaoyu Liu, Lei Shi, Bing Han, Tianyu Zhai, Mingyue Liu, Yujing Sun, Hongwei Xu

“…Methods We retrospectively collected the data from January 2020 to October 2021 in our hospital and randomly divided the patients into training and validation sets in an 8:2 ratio. We used multiple machine learning algorithms such as logistic regression (LR), Decision tree, Support Vector Machine, Random forest, and so on to establish the models. …”
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Accurate prediction of mediolateral episiotomy risk during labor: development and verification of an artificial intelligence model by Tingting Hu, Liheng Zhao, Xueling Zhao, Lin He, Xiaoli Zhong, Zhe Yin, Junjie Chen, Yanting Han, Ka Li

“…The factors affecting mediolateral episiotomy were determined based on the expert consultation method. Six machine learning models, namely Logistic regression(LR), Support Vector Machine(SVM), K-Nearest Neighb(KNN), Random Forest (RF), Light Gradient Boosting Machine(LightGBM), and eXtreme Gradient Boosting‌(XGBoost) were constructed on this basis. …”
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Integrated Bioinformatics and Experimental Validation Reveal Macrophage Polarization-Related Biomarkers for Osteoarthritis Diagnosis by He Q, Liu L, Hu X, Lin L, Song Z, Xia Y, Lin Q, Wei J, Li S

“…Least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to identify hub genes and construct a diagnostic model validated through internal datasets and multiple external bulk RNA-seq and single-cell RNA-seq data. …”
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Accurate Estimation of Plant Water Content in Cotton Using UAV Multi-Source and Multi-Stage Data by Shuyuan Zhang, Haitao Jing, Jihua Dong, Yue Su, Zhengdong Hu, Longlong Bao, Shiyu Fan, Guldana Sarsen, Tao Lin, Xiuliang Jin

“…In this study, we used UAV-based data, including texture features, vegetation indices, and a heat index, and applied four machine learning algorithms, i.e., partial least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XGB), to estimate CWC. …”
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Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture Classification by Zhuofu Liu, Zihao Shu, Vincenzo Cascioli, Peter W. McCarthy

“…The k-nearest neighbor (KNN) algorithm demonstrated superior performance over Decision Tree, Discriminant Analysis, Naive Bayes, and Support Vector Machine (SVM). …”
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Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization by Chen Fei, Zhuo Lu, Weiwei Jiang, Liang Zhao, Fan Zhang

“…Experimental results demonstrate that the proposed XGBoost–ARIMA model outperforms traditional algorithms, including Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), not only in estimation accuracy but also in generalization capability, showing significant improvements over five other regression models.…”
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Optimizing maize germination forecasts with random forest and data fusion techniques by Lili Wu, Yuqing Xing, Kaiwen Yang, Wenqiang Li, Guangyue Ren, Debang Zhang, Huiping Fan

“…Features such as color, shape, texture, crack count, and normalized voltage were used to form feature vectors. Various prediction algorithms, including random forest (RF), radial basis function (RBF), neural networks (NNs), support vector machine (SVM), and extreme learning machine (ELM), were developed and tested against standard germination experiments. …”
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Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods by Longlin Jiang, Kexun Li, Simiao Lu, Zhou Hong, Yifang Wang, Qin Xie, Qin He, Sirui Wei, Aoru Zhou, Hong Kang, Xuefeng Leng, Qing Yang, Yan Miao

“…Traditional regression techniques, including Ordinary Least Squares regression, Generalized Linear Model, as well as machine learning techniques, such as Gradient Boosting Tree, Support Vector Regression, Ridge Regression are used. …”
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A hybrid VMD-LSTM-SVR model for landslide prediction by Nianhong Wang, Meijun Wang, Jun Zhang

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Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction by Xidi Yang, Liangyu Zhu, Wenyu Jiang, Yiting Yang, Mailin Gan, Linyuan Shen, Li Zhu

“…This study explores the feasibility of predicting long-term FCR using short-term FCR data based on machine learning techniques. We employed nineteen machine learning algorithms, including Linear Regression, support vector machines (SVMs), and Gradient Boosting, using historical datasets to train and validate the models. …”
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A machine learning-based depression risk prediction model for healthy middle-aged and older adult people based on data from the China health and aging tracking study by Fang Xia, Jie Ren, Linlin Liu, Yanyin Cui, Yufang He

“…Depression was assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10), with a score of ≥10 indicating depressive symptoms. Several machine learning algorithms, including logistic regression, k-nearest neighbor, support vector machine, multilayer perceptron, decision tree, and XGBoost, were employed to predict the 2-year depression risk. …”
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