Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers by Hongqi Zhou, Weiyun Jin, Lindi Li, Xiangwen Nie, Weiwei Wu, Ran Chen, Qizhen Xie, Haixia Wu, Weiwei Jiang, Min Tang, Jinhai Wang, Maoyuan Wang

“…Finally, the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm was used to optimize feature variables. …”
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Data driven models for predicting pH of CO2 in aqueous solutions: Implications for CO2 sequestration by Mohammad Rasool Dehghani, Moein Kafi, Hamed Nikravesh, Maryam Aghel, Erfan Mohammadian, Yousef Kazemzadeh, Reza Azin

“…To fill this research gap, this study developed 15 models comprising five machine learning methods: regression trees, support vector regression, Gaussian process regression, bagged trees, and boosted trees, and three optimization algorithms: random search, grid search, and Bayesian optimization. …”
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Spectral estimation of the aboveground biomass of cotton under water–nitrogen coupling conditions by Shunyu Qiao, Jiaqiang Wang, Fuqing Li, Jing Shi, Chongfa Cai

“…Support vector machine (SVM), regression tree (RT), and convolutional neural network (CNN) were employed to verify the accuracy. …”
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Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis by XinPei Liu, SanXi Ai, RuiMing Yu, ChaoJi Zhang, Qi Miao

“…Among the six algorithms tested, the radial basis function support vector machine (RBF-SVM) had the highest AUC at 0.771. …”
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Predicting the time to get back to work using statistical models and machine learning approaches by George Bouliotis, M. Underwood, R. Froud

“…We explored predictors of time to return to work with proportional hazards (Semi-Parametric Cox in Stata) and (Flexible Parametric Parmar-Royston in Stata) against the Survival penalised regression with Elastic Net penalty (scikit-survival), (conditional) Survival Forest algorithm (pySurvival), and (kernel) Survival Support Vector Machine (pySurvival). …”
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Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting by Tamrat Endebu, Girma Taye, Wakgari Deressa

“…Six supervised ML classifiers—J48 decision tree, random forest, K-nearest neighbors, support vector machine, logistic regression, and naïve Bayes—were utilized for training via Weka 3.8.6 software. …”
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A temporal fusion method for modeling the rate of penetration during deep geological drilling by Yang ZHOU, Chengda LU, Min WU, Xin CHEN, Ningping YAO, Haitao SONG, Youzhen ZHANG

“…Initially, a basic ROP model was constructed using support vector regression (SVR) to solve the nonlinear problem caused by ROP changes. …”
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Cellular senescence defining the disease characteristics of Crohn’s disease by Wenyu Zhang, Xianzong Ma, Xianzong Ma, Wenqing Tian, Yongsheng Teng, Meihua Ji

“…However, the role of CS in the pathogenesis and diagnosis prediction of CD are still unknown.MethodsWe utilized CD-related datasets from the GEO database for differential gene expression analysis, and CS related differentially expressed genes (CSRDEGs) in CD by a comprehensive bioinformatics analysis encompassing GSEA, WGCNA, and various interaction networks. The support vector machine (SVM) algorithm, random forest algorithm and LASSO regression analysis was used to construct a diagnostic model. …”
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Leveraging machine learning for prediction and optimization of texture properties of sustainable activated carbon derived from waste materials by Ahmed Farid Ibrahim, Mohamed Abdrabou Hussein

“…Random Forest (RF), Decision Tree (DT), Gradient Boosting Regressor (GBR), support vector machines (SVM), and Artificial Neural Networks (ANN) were applied along with key input parameters, including raw material type, particle size, and activation conditions. …”
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Machine Learning Based Classification for Spam Detection by Onur Sevli, Serkan Keskin

“…In this study, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms are used to classify spam emails and the results are compared. …”
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Rapid Lactic Acid Content Detection in Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging by Xiaoyu Xue, Haiqing Tian, Kai Zhao, Yang Yu, Ziqing Xiao, Chunxiang Zhuo, Jianying Sun

“…To minimize model redundancy, three algorithms, such as competitive adaptive reweighted sampling (CARS), were used to extract the characteristic wavelengths of the three dyes, and the combination of the characteristic wavelengths obtained by each algorithm was used as an input variable to build an analytical model for quantitative prediction of the lactic acid content by support vector regression (SVR). …”
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C-reactive protein-triglyceride glucose index predicts stroke incidence in a hypertensive population: a national cohort study by Songyuan Tang, Han Wang, Kunwei Li, Yaqing Chen, Qiaoqi Zheng, Jingjing Meng, Xin Chen

“…The Support Vector Machine (SVM) survival model exhibited the best predictive performance for stroke risk in hypertensive patients, with an area under the curve (AUC) of 0.956. …”
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Machine Learning-Based Prediction of Postoperative Deep Vein Thrombosis Following Tibial Fracture Surgery by Humam Baki, İsmail Bülent Özçelik

“…A total of 42 predictive models were developed using combinations of six ML algorithms—logistic regression, support vector machine, random forest, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), and neural networks—and seven feature selection methods, including SHapley Additive exPlanations (SHAP), Least Absolute Shrinkage and Selection Operator (LASSO), Boruta, recursive feature elimination, univariate filtering, and full-variable inclusion. …”
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Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers by Nguyen Le BPharm, Sola Han PhD, Ahmed S. Kenawy MS, Yeijin Kim MS, Chanhyun Park PhD

“…MACE included acute myocardial infarction, ischemic heart disease, stroke, heart failure, revascularization, malignant arrhythmias, and cardiovascular-related death. Six ML algorithms (L2-Logistic regression, Support Vector Machine, Complement Naïve Bayes, Random Forest, XGBoost, and CatBoost) were trained on 2017-2018 data and tested on 2019 data. …”
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A secure IoT-edge architecture with data-driven AI techniques for early detection of cyber threats in healthcare by Mamta Kumari, Mahendra Gaikwad, Salim A. Chavan

“…The investigation makes use of an eight variety of machine learning models, including feedforward neural networks, gradient boosting machines (XGBoost and LightGBM), logistic regression, VAEs, random forests, along with support vector machines (SVM). …”
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Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis by Man Sun, Jianzhang Wang, Ping Xu, Libo Zhu, Gen Zou, Shuyi Chen, Yuanmeng Liu, Xinmei Zhang

“…Radiomic features were extracted from MRI-T2 image. Random forest algorithm was used to select the key radiomics features of different symptoms and develop the radiomic model by support vector machine algorithm. …”
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Construction and validation of a machine learning-based nomogram model for predicting pneumonia risk in patients with catatonia: a retrospective observational study by Yi-chao Wang, Qian He, Yue-jing Wu, Li Zhang, Sha Wu, Xiao-jia Fang, Shao-shen Jia, Fu-gang Luo

“…Patients were divided into catatonia with and without pneumonia groups. The LASSO Algorithm was used for feature selection, and seven machine learning models: Decision Tree(DT), Logistic Regression(LR), Naive Bayes(NB), Random Forest(RF), K Nearest Neighbors(KNN), Gradient Boosting Machine(GBM), Support Vector Machine(SVM) were trained. …”
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Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data by Denglin Li, Luxin Zhang, Lifei Xu, Renhe Zhai, Hanyu Gao, Junlan Gao, Minghai Wei, Ningwei Che, Yeting He

“…Predictive models for OS and CSS were established based on eight machine learning algorithms, including Lasso Cox, random survival forest, CoxBoost, generalized boosted regression modelling (GBM), stepwise Cox and survival support vector machine, eXtreme Gradient Boosting, supervised principal component and partial least squares regression for Cox, and the selected predictive models were evaluated by the area under the ROC curves (AUC) and 95% confidence interval (CI), calibration curves and decision curve analyses in the training set, validation set and test set. …”
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Plasma metabolite biomarker identification study for the early detection of gastric cancer by Juan Zhu, Yida Huang, Bin Liu, Xue Li, Li Yuan, Le Wang, Kun Qian, Yingying Mao, Lingbin Du, Xiangdong Cheng

“…Five machine learning algorithms (neural network, support vector machine, ridge regression, lasso regression and Naïve Bayes) were used to build a diagnostic model. …”
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A mineral-strength conversion model based on LIBS technology and rapid batch testing and application of uniaxial compressive strength by Qinghe ZHANG, Xiaorui WANG, Chuanbing WANG, Weiguo LI, Fengqiang GONG, Xinsheng ZHANG, Jingguo LI

“…The mass fraction of mineral components is analyzed via the support vector regression (SVR) algorithm. In the end, a mineral-strength conversion model is established to calculate the UCS from the predicted values of mineral component concentrations, and its rationality and scientific validity are validated by the standard mechanical tests. …”
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