Predicting major amputation risk in diabetic foot ulcers using comparative machine learning models for enhanced clinical decision-making by Zixuan Liu, Dehua Wei, Jiangning Wang, Lei Gao

“…Subsequently, risk prediction models were independently developed by using these feature variables based on six machine learning algorithms: logistic regression, random forest, support vector machine, K-nearest neighbors, gradient boosting machine (GBM), and extreme gradient boosting (XGBoost). …”
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Machine learning-based prediction of optimal antenatal care utilization among reproductive women in Nigeria by Jamilu Sani, Adeyemi Oluwagbemiga, Mohamed Mustaf Ahmed

“…After data preprocessing and feature selection, six supervised ML algorithms—Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and XGBoost—were applied using Python 3.9. …”
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Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches by Jiayi Zhang, Zhixiang Jia, Jiahui Zhang, Xiaohui Mu, Limei Ai

“…Using the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms, we screened for seven potential diagnostic biomarkers with strong diagnostic capabilities: SMAD3, IL7R, IL18, FAS, CD5, CCR7, and CSF1R. …”
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Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival by Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz

“…Several machine learning algorithms, including Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines (SVM), were evaluated for predictive accuracy. …”
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The Analytical System for Determining the Attitude of Students to the University by Violeta Tretynyk, Mariia Pinda

“…Existing software solutions use methods for processing and analyzing text tone based on machine learning methods and algorithms (naive Bayesian classifier, support vector machine, logistic regression), as well as deep learning (recurrent neural networks). …”
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PROACTIVE MITIGATION OF DDoS IoT-RELATED ATTACK USING MACHINE LEARNING AND SOFTWARE DEFINED NETWORKING TECHNIQUES by Emmanuel J. Ebong, Samuel N. John, Dominic S. Nyitamen, Samuel F. Kolawole

“…The large dataset was scaled down using Min Max Scaler before the Machine Learning (ML) classification stage. Four (4) ML algorithms namely, Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) were used to classify the models. …”
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Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning by Milad Vahidi, Sanaz Shafian, William Hunter Frame

“…Our results demonstrate that PCA effectively detected critical variables for soil moisture estimation, with the ANN model outperforming other machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (XGBoost). …”
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Impact of PM_2.5 Pollution on Solar Photovoltaic Power Generation in Hebei Province, China by Ankun Hu, Zexia Duan, Yichi Zhang, Zifan Huang, Tianbo Ji, Xuanhua Yin

“…To capture these complex aerosol–radiation–PV interactions, we developed and compared the following six machine learning models: Support Vector Regression, Random Forest, Decision Tree, K-Nearest Neighbors, AdaBoost, and Backpropagation Neural Network. …”
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Stroke risk prediction: a deep learning approach for identifying high-risk patients by Afeez A. Soladoye, Kazeem M. Olagunju, Sunday A. Ajagbe, Ibrahim A. Adeyanju, Precious I. Ogie, Pragasen Mudali

“…The developed system outperformed other ML algorithms like LSTM, GRU-LSTM, Support Vector Machine (SVM) and Logistic Regression. …”
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Ensemble stacked model for enhanced identification of sentiments from IMDB reviews by Komal Azim, Alishba Tahir, Mobeen Shahroz, Hanen Karamti, Annia Almeyda Vazquez, Angel Rojas Vistorte, Imran Ashraf

“…In this regard, an ensemble model RRLS is proposed that stacks random forest, recurrent neural network, logistic regression (LR), and support vector machine (SVM). The Internet Movie Database (IMDB) movie reviews and Urdu tweets are examined in this study using Urdu sentiment analysis. …”
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Identification and mechanistic insights of cell senescence-related genes in psoriasis by Guiyan Deng, Cheng Xu, Dunchang Mo

“…Protein-protein interaction networks, Gene Ontology, and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to explore the functions and pathways of these genes. Machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support vector machine-recursive feature elimination (SVE-RFE), were used to select feature genes that were validated by qRT-PCR. …”
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Machine Learning–Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors by Mehmet Taştan

“…To improve sensor accuracy, eight different machine learning (ML) algorithms were applied: Decision Tree (DT), Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (kNN), AdaBoost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), and Stochastic Gradient Descent (SGD). …”
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Integrated network toxicology, machine learning and molecular docking reveal the mechanism of benzopyrene-induced periodontitis by Wen Wenjie, Li Rui, Zhuo Pengpeng, Deng Chao, Zhang Donglin

“…Three machine learning algorithms (Support Vector Machine, Random Forest, and LASSO regression) were applied for core target identification, followed by Molecular docking analyses. …”
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Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas by Yue Hu, Xin Cao, Hongyi Chen, Daoying Geng, Kun Lv

“…The patients were stratified and randomized into the training and testing datasets with a 7:3 ratio. The logical regression, random forest, support vector machine (SVM) and adaptive boosting algorithms were used to establish models. …”
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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

“…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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Assessment and Modeling of Green Roof System Hydrological Effectiveness in Runoff Control: A Case Study in Dublin by Mehdi Gholamnia, Payam Sajadi, Salman Khan, Srikanta Sannigrahi, Saman Ghaffarian, Himan Shahabi, Francesco Pilla

“…The findings are compelling, with Support Vector Regression (SVR) achieving R2 values ranging from 0.67 to 0.82 and RMSE values ranging from 0.37 to 1.51 millimeters for WRA, TRUV, PRD, and PFR. …”
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A novel machine learning models for meteorological drought forecasting in the semi-arid climate  region by Chaitanya Baliram Pande, Dinesh Kumar Vishwakarma, Aman Srivastava, Kanak N. Moharir, Fahad Alshehri, Norashidah Md Din, Lariyah Mohd Sidek, Bojan Đurin, Abebe Debele Tolche

“…Given the limited studies on ensemble and Machine Learning (ML) models for drought forecasting, this research compares five ML models [Robust Linear Regression, Bagged Trees, Boosted Trees, Support Vector Machine (SVM), and Matern Gaussian Process Regression (GPR)] to determine superior accuracy in the regional context. …”
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Development and validation of an interpretable machine learning model for retrospective identification of suspected infection for sepsis surveillance: a multicentre cohort studyRes... by Renée A.M. Tuinte, Luuk P.J. Smolenaers, Bram T. Knoop, Konstantin Föhse, Tamar J. van der Aart, Hjalmar R. Bouma, Mihai G. Netea, Katrijn Van Deun, Jaap ten Oever, Jacobien J. Hoogerwerf

“…Seven ML methods, including gradient boosting, random forest, logistic regression, decision trees, support vector machines, K nearest neighbours and stochastic gradient descent, were trained to identify sepsis with manual chart review as reference standard. …”
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Construction of a sugar and acid content estimation model for Miliang-1 kiwifruit during storage by LIU Li, YANG Tianyi, DONG Congying, SHI Caiyun, SI Peng, WEI Zhifeng, GAO Dengtao

“…The regression models utilized a combination of Support Vector Regression (SVR) and Backpropagation Neural Network (BP) algorithms to determine the optimal predictive performance for each quality indicator. …”
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Multi-Target Mechanism of Compound Qingdai Capsule for Treatment of Psoriasis: Multi-Omics Analysis and Experimental Verification by Qiao Y, Li C, Chen C, Wu P, Yang Y, Xie M, Liu N, Gu J

“…Machine learning, including Lasso regression, Random Forest, and Support Vector Machine (SVM), were utilized to screen core targets of psoriasis. …”
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