Machine learning-based predictive modeling of angina pectoris in an elderly community-dwelling population: Results from the PoCOsteo study. by Shahrokh Mousavi, Zahrasadat Jalalian, Sima Afrashteh, Akram Farhadi, Iraj Nabipour, Bagher Larijani

“…We developed the following models: logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (KNN), linear and quadratic discriminant analysis (LDA, QDA), decision tree (DT), and two ensemble models: random forest (RF) and adaptive boosting (AdaBoost). …”
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Systemic immune-inflammatory biomarkers combined with the CRP-albumin-lymphocyte index predict surgical site infection following posterior lumbar spinal fusion: a retrospective stu... by Zixiang Pang, Jiawei Liang, Jiayi Chen, Yangqin Ou, Qinmian Wu, Shengsheng Huang, Shengbin Huang, Yuanming Chen

“…Feature selection via univariate regression analysis identified predictive variables, followed by model development using ten machine learning algorithms: logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), XGBoost, neural network, K-nearest neighbors(KNN), AdaBoost, LightGBM, and CatBoost. …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…The CNN model achieved an accuracy of 85 %, outperforming traditional models, such as Logistic Regression (78 %) and Support Vector Machines (80 %). …”
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Integration of agronomic information, vegetation indices (VIs), and meteorological data for phenological monitoring and yield estimation of rice (Oryza sativa L.) by Jorge A. Fernandez-Jibaja, Nilton Atalaya-Marin, Yeltsin A. Álvarez-Robledo, Victor H. Taboada-Mitma, Juancarlos Cruz-Luis, Daniel Tineo, Malluri Goñas, Darwin Gómez-Fernández

“…For yield estimation, feature selection was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) to increase model efficiency and interpretability. Among the regression algorithms tested, support vector regression (SVR) demonstrated the highest predictive accuracy (R² = 0.81) for the Bellavista variety at the maximum tillering stage. …”
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Development and validation of a machine learning-based risk prediction model for stroke-associated pneumonia in older adult hemorrhagic stroke by Yi Cao, Yi Cao, Haipeng Deng, Shaoyun Liu, Xi Zeng, Yangyang Gou, Weiting Zhang, Yixinyuan Li, Hua Yang, Min Peng

“…Among the four machine learning algorithms evaluated [XGBoost, Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes], the LR model demonstrated robust and consistent performance in predicting SAP among older adult patients with hemorrhagic stroke across multiple evaluation metrics. …”
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TELEPROM Psoriasis: Enhancing patient-centered care and health-related quality of life (HRQoL) in moderate-to-severe plaque psoriasis by Gabriel Mercadal-Orfila, Gabriel Mercadal-Orfila, Piedad López Sánchez, Aranzazu Pou Alonso, Olatz Ibarra-Barrueta, Emilio Monte-Boquet, Joaquin Borrás Blasco, Nuria Padullés Zamora, Patricia Sanmartin-Fenollera, Cristina Capilla Montes, M. Ángeles Bernabéu Martínez, Salvador Herrera-Pérez

“…Machine learning models, particularly Random Forest (AUC = 0.98) and Support Vector Machine (AUC = 0.96), effectively predicted patient engagement. …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…Again, this research used a strong methodology by incorporating Machine learning (ML) algorithms, such as: Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Linear Regression Model (LRM), were applied to forecast and confirm the quality of the water. …”
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Comparison of 46 Cytokines in Peripheral Blood Between Patients with Papillary Thyroid Cancer and Healthy Individuals with AI-Driven Analysis to Distinguish Between the Two Groups by Kyung-Jin Bae, Jun-Hyung Bae, Ae-Chin Oh, Chi-Hyun Cho

“…As AI classification algorithms to categorize patients with PTC, K-nearest neighbor function, Naïve Bayes classifier, logistic regression, support vector machine, and eXtreme Gradient Boosting (XGBoost) were employed. …”
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Prediction of canopy mean traits in herbaceous plants by the UAV multispectral data: The quest for a better leaf-to-canopy upscaling method by Yuanqi Shan, Yunlong Yao, Lei Wang, Zhihui Wang, Huaihu Yi, Yi Fu, Weineng Li, Xuguang Zhang, Wenji Wang, Zhongwei Jing

“…This study proposed a novel approach for calculating canopy mean traits using the geometric mean method and compared its performance to that of the CWM methods in combination with three modeling algorithms Partial Least Squares Regression (PLSR), Random Forest regression (RF), and Support Vector Machine regression (SVM). …”
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Protecting Industrial Control Systems From Shodan Exploitation Through Advanced Traffic Analysis by Sayed Reza Ghazinour Naeini, Alireza Shameli-Sendi, Masoume Jabbarifar

“…Several machine learning algorithms were evaluated, including Random Forest, Support Vector Machine, Logistic Regression, and Gradient Boosting. …”
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Enhanced Spring Wheat Soil Plant Analysis Development (SPAD) Estimation in Hetao Irrigation District: Integrating Leaf Area Index (LAI) Under Variable Irrigation Conditions by Qiang Wu, Dingyi Hou, Min Xie, Qi Gao, Mengyuan Li, Shuiyuan Hao, Chao Cui, Keke Fan, Yu Zhang, Yongping Zhang

“…This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. …”
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The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules by Zuhua Song, Qian Liu, Jie Huang, Dan Zhang, Jiayi Yu, Bi Zhou, Jiang Ma, Ya Zou, Yuwei Chen, Zhuoyue Tang

“…Recursive feature elimination was employed for variable selection. Three ML algorithms—support vector machine (SVM), logistic regression (LR), and naive Bayes (NB)—were implemented to construct predictive models. …”
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Integrating LoRaWAN sensor network and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United State by Andres Perea, Sajidur Rahman, Huiying Chen, Andrew Cox, Shelemia Nyamuryekung’e, Mehmet Bakir, Huping Cao, Richard Estell, Brandon Bestelmeyer, Andres F. Cibils, Santiago A. Utsumi

“…Behavioral observations were made using 168 h of video records, which resulted in a dataset of 9222 instances of labeled sensor data. Logistic regression, support vector machine, multilayer perceptron, XGBoost and random forest algorithms were trained and tested. …”
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The influence of pH and temperature on benthic chlorophyll-a: Insights from SHAP-XGBoost and random forest models by Sangar Khan, Noël P.D. Juvigny-Khenafou, Tatenda Dalu, Paul J. Milham, Yasir Hamid, Kamel Mohamed Eltohamy, Habib Ullah, Bahman Jabbarian Amiri, Hao Chen, Naicheng Wu

“…We employed Random Forest (RF), eXtreme gradient boosting (XGBoost) and SHAP-enhanced eXtreme gradient boosting (SHAP XGBoost) models, alongside Support Vector Regression (SVR), to predict chl–a levels in diverse reaches and identify the key determinants. …”
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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 machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…The framework integrated diverse ML algorithms, including Linear Regression (LR), Decision Trees (DT), Support Vector Machine (SVM), Random Forest (RF), Balanced Bagging (BG), Gradient Boosting (GB), and Convolutional Neural Networks (CNNs). …”
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Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

“…Additionally, support vector regression (SVR) was used to predict chl-a concentrations across upstream, midstream, and downstream sections. …”
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Integrative analysis identifies IL-6/JUN/MMP-9 pathway destroyed blood-brain-barrier in autism mice via machine learning and bioinformatic analysis by Cong Hu, Heli Li, Jinru Cui, Yunjie Li, Feiyan Zhang, Hao Li, Xiaoping Luo, Yan Hao

“…Through integrative analysis combining differential gene expression profiling with three machine learning algorithms - Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and RandomForest combined with eXtreme Gradient Boosting (XGBoost) - we identified four hub genes, with JUN emerging as a core regulator. …”
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Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells by Tianxiang Zhang, Chunhui Yuan, Mo Chen, Jinjiang Liu, Wei Shao, Ning Cheng

“…Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were used to screen for overlapping FRDEGs in CS and AS. …”
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NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region by Emna Ayari, Zeineb Kassouk, Zohra Lili-Chabaane, Nadia Ouaadi, Nicolas Baghdadi, Mehrez Zribi

“…Relative low accuracy characterizes the regression algorithms’ estimations when NDVI ≥ 0.4 compared to their performance during the aforementioned periods. …”
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