Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database by Yupeng Han, Xiyuan Xie, Jiapeng Qiu, Yijie Tang, Zhiwei Song, Wangyu Li, Xiaodan Wu, Xiaodan Wu

“…Feature variables were selected using the LASSO-Boruta combined algorithm, and five machine learning (ML) models, including Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost),Light Gradient Boosting Machine(LGBM), Multilayer Perceptron (MLP), and Support Vector Machines (SVM), were subsequently developed using these variables. …”
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Building a composition-microstructure-performance model for C–V–Cr–Mo wear-resistant steel via the thermodynamic calculations and machine learning synergy by Shuaiwu Tong, Shuaijun Zhang, Chong Chen, Tao Jiang, Peng Li, Shizhong Wei

“…By using phase content and experimental parameters as input features, the Gradient Boosted Tree model and Support Vector Regression model demonstrated strong applicability in predicting frictional performance and wear, respectively. …”
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A predictive model for functional cure in chronic HBV patients treated with pegylated interferon alpha: a comparative study of multiple algorithms based on clinical data by Ya-mei Ye, Yong Lin, Fang Sun, Wen-yan Yang, Lina Zhou, Chun Lin, Chen Pan

“…Subsequently, predictive models were developed via logistic regression, random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and support vector machine (SVM) algorithms. The efficacy of these models was assessed through various performance metrics, including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and F1 score. …”
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SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study by Abrar Yaqoob, Navneet Kumar Verma, Mushtaq Ahmad Mir, Ghanshyam G. Tejani, Nashwa Hassan Babiker Eisa, Hind Mamoun Hussien Osman, Mohd Asif Shah

“…To evaluate the effectiveness of the proposed method, we compared it with other classifiers, including Linear Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). …”
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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective S... by Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

“…Model 3 added breast sonography response data to the clinical variables in model 1. Algorithms including logistic regression, random forest, support vector machines, and extreme gradient boosting were used. …”
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AI and Machine Learning in V2G technology: A review of bi-directional converters, charging systems, and control strategies for smart grid integration by Nagarajan Munusamy, Indragandhi Vairavasundaram

“…We explore the potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to optimize V2 G performance. …”
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An explainable predictive machine learning model for axillary lymph node metastasis in breast cancer based on multimodal data: A retrospective single-center study by Yuxi liu, Yunfeng Wu, Qing Xia, Hao He, Haining Yu, Ying Che

“…Materials and methods A retrospective study was conducted on clinical data from 401 patients with pathologically confirmed breast cancer. Ten machine learning algorithms—including Naïve Bayes, Random Forest, Logistic Regression, and Support Vector Machines—were implemented to construct predictive models. …”
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Sentiment Analysis of Netizens on Constitutional Court Rulings in the 2024 Presidential Election by Wahyudi Ariannor, Sami M A B Alshalwi, Budi Susarianto

“…This research explores sentiment analysis of the Constitutional Court’s decisions, especially in the context of the presidential election, using the Support Vector Machine (SVM), Logistic Regression, and Naive Bayes algorithms. …”
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Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning by Josh Netter, Kyriakos G. Vamvoudakis, Timothy F. Walsh, Jaideep Ray

“…In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. …”
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Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control by Miguel-Angel Gil-Rios, Ivan Cruz-Aceves, Arturo Hernandez-Aguirre, Martha-Alicia Hernandez-Gonzalez, Sergio-Eduardo Solorio-Meza

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Advanced Computational Methods for Mitigating Shock and Vibration Hazards in Deep Mines Gas Outburst Prediction Using SVM Optimized by Grey Relational Analysis and APSO Algorithm by Xiang Wu, Zhen Yang, Dongdong Wu

“…Moreover, adaptive particle swarm optimization (APSO) was used to optimize the penalty factor and kernel parameters of the support vector machine to improve the global search ability and avoid the occurrence of the local optimal solutions. …”
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Optimizing drying and storage for edible mushrooms: Study on gamma irradiation levels, drying temperatures, and packaging materials with SVM-based predictions by Ehsan Fartash Naeimi, Mohammad Hadi Khoshtaghaza, Kemal Çağatay Selvi, Mariana Ionescu, Soleiman Abbasi

“…Packaging materials and drying conditions significantly affected (P < 0.01) texture firmness, while packaging showed no significant effect (P > 0.01) on L∗. The Support Vector Machine (SVM) algorithm accurately predicted change in L∗ and texture firmness after six months of storage, with the Pearson universal kernel producing the highest correlation coefficients (0.996, 1.000, and 1.000). …”
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Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments by G. Nalinipriya, S. Rama Sree, K. Radhika, E. Laxmi Lydia, Faten Khalid Karim, Mohamad Khairi Ishak, Samih M. Mostafa

“…Various statistical and ML approaches, like Bayesian classification, deep learning (DL), and support vector machines (SVM), have efficiently alleviated cyber threats. …”
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Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds by Vittoria Asti, Michela Ablondi, Arnaud Molle, Andrea Zanotti, Matteo Vasini, Alberto Sabbioni

“…The positive correlation between judge evaluations and sensor data indicates judges’ ability to evaluate overall gait quality. Three different algorithms were employed to predict the judges score from the IMU measurements: Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN). …”
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Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis by Pang Z, Ou Y, Liang J, Huang S, Chen J, Huang S, Wei Q, Liu Y, Qin H, Chen Y

“…In the test group, logistic regression analysis, support vector machine (SVM), random forest (RF), decision tree (DT), XGboost, Naïve Bayes (NB), k-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) were used to identify specific variables. …”
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A KNN-based model for non-invasive prediction of hemorrhagic shock severity in prehospital settings: integrating MAP, PBUCO2, PTCO2, and PPV by Peng Zhao, Wencai Pan, Xin Zou, Jiaqing Yang, Shihui Zhang, Yufei Liu, Yang Li

“…Meanwhile, a prediction model based on the support vector machine (SVM) algorithm was established. …”
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An Integrated Learning Approach for Municipal Solid Waste Classification by Hieu M. Sondao, Tuan M. Le, Hung V. Pham, Minh T. Vu, Son Vu Truong Dao

“…These selected features are then fed into machine learning classifiers&#x2014;Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbor (KNN)&#x2014;for final predictions. …”
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Improving network security using keyboard dynamics: A comparative study by Ugwunna, C.O., Chukwuogo, O.E., Alabi, O.A., Kareem, M.K., Belonwu, T.S., Oloyede, S.O.

“…According to the results, the accuracy of the Random Forest, Support Vector Machine, KNN, and Decision Tree algorithms are, respectively, 98, 97.55, 97.28, and 94.26%. …”
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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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Machine Learning-Based Prediction of Unconfined Compressive Strength of Sands Treated by Microbially-Induced Calcite Precipitation (MICP): A Gradient Boosting Approach and Correlat... by Saeed Talamkhani

“…The finding demonstrates that the gradient boosting method outperformed five commonly used machine learning algorithms (artificial neural networks, random forests, k-nearest neighbors, support vector regression, and decision trees) in predicting the UCS of biocemented sands. …”
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