Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens by Cheng Han, Shiying Lu, Pan Hu, Jiang Chang, Deying Zou, Feng Li, Yansong Li, Qiang Lu, Honglin Ren

“…Integrating these genes into an ML model based on the support vector machine (SVM) algorithm allows us to predict the zoonotic potential of various Brucella strains with high accuracy. …”
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Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study by Xiangkui Jiang, Bingquan Wang

“…Subsequently, we constructed 6 predictive models using different algorithms: logistic regression, support vector machine, gradient boosting machine, Extreme Gradient Boosting, multilayer perception, and graph convolutional networks. …”
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Evaluation of classification algorithms in the Google Earth Engine platform for the identification and change detection of rural and periurban buildings from very high-resolution i... by Alejandro Coca-Castro, Maycol A. Zaraza-Aguilera, Yilsey T. Benavides-Miranda, Yeimy M. Montilla-Montilla, Heidy B. Posada-Fandiño, Angie L. Avendaño-Gomez, Hernando A. Hernández-Hamon, Sonia C. Garzón-Martinez, Carlos A. Franco-Prieto

“…In total, eight traditional classification algorithms, three unsupervised (K-means, X-Means y Cascade K-Means) and five supervised (Random Forest, Support Vector Machine, Naive Bayes, GMO maximum Entropy and Minimum distance) available at GEE were trained. …”
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Combination ATR-FTIR with Multiple Classification Algorithms for Authentication of the Four Medicinal Plants from <i>Curcuma</i> L. in Rhizomes and Tuberous Roots by Qiuyi Wen, Wenlong Wei, Yun Li, Dan Chen, Jianqing Zhang, Zhenwei Li, De-an Guo

“…The results showed that support vector machine (SVM) modeling was superior to other models and the accuracy of validation and prediction was preferable, with a modeling time of 127.76 s. …”
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Development of a machine learning prognostic model for early prediction of scrub typhus progression at hospital admission based on clinical and laboratory features by Youguang Lu, Zixu Wang, Junhu Wang, Yingqing Mao, Chuanshen Jiang, Jinpiao Wu, Haizhou Liu, Haiming Yi, Chao Chen, Wei Guo, Liguan Liu, Yong Qi

“…Additionally, a simplified model based on Support Vector Machine was constructed and evaluated as an alternative optimal model.Conclusions This study is the first to use machine learning algorithms to accurately predict the developments of ST patients upon admission to hospitals. …”
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An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data by Amena Mahmoud, Eiko Takaoka

“…The selected features were then used to train a stacking ensemble model, which combined multiple base learners, including Multi-Layer Perceptron (MLP), Random Forest (RF) model, K-nearest neighbor (KNN) model, and Support vector machine (SVM), with a meta-learner Extreme Gradient Boosting (Xgboost) model to make final predictions. …”
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Prediction of persistent type II endoleak after endovascular aortic repair using machine learning based on preoperative clinical data and radiomic by Jinqing Mo, Qi Liu, Kangjie Wang, Lin Huang, Chen Yao

“…RESULTS: Among the initial 1006 preoperative clinical and radiomic features, 12 features were selected for machine learning model development. The support vector machines (SVMs) classifier performed well in both the training and test sets, with area under the curve values of 0.994 (95% confidence interval [CI], 0.967–1) and 0.970 (95% CI, 0.901–1), the sensitivity of 0.927 and 0.882, specificity of 0.979 and 0.920, and accuracy of 0.966 and 0.848, respectively. …”
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Comprehensive flexible framework for using multi-machine learning methods to optimal dynamic transient stability prediction by considering prediction accuracy and time by Ali Abdalredha, Alireza Sobbouhi, Abolfazl Vahedi

“…To show the effectiveness of the proposed framework, for instance, four different ML approaches are used: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-nearest neighbor (KNN). …”
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Machine learning-based spatio-temporal assessment of land use/land cover change in Barishal district of Bangladesh between 1988 and 2024 by Walida Zaman, H Rainak Khan Real

“…The performance of four machine learning algorithms (Support Vector Machine, Classification and Regression Tree, K-Nearest Neighbor, and Random Forests) were evaluated to ensure classification reliability. …”
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Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging by Hakan Ayyıldız, Okan İnce, Esin Korkut, Merve Gülbiz Dağoğlu Kartal, Atadan Tunacı, Şükrü Mehmet Ertürk

“…Once the features were extracted, Pearson’s correlation coefficient and selection were performed using wrapper-based sequential algorithms. The models were then built using support vector machine (SVM) and logistic regression (LR) machine learning algorithms. …”
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Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy by Georgina Company-Se, Virginia Pajares, Albert Rafecas-Codern, Pere J. Riu, Javier Rosell-Ferrer, Ramon Bragós, Lexa Nescolarde

“…Decision Tree, Support Vector Machines (SVM), Ensemble Method, K-Nearest Neighbors, Naïve Bayes and Discriminant Analysis were applied using mean averaged bioimpedance modulus and phase angle spectra from lung tissue across 15 frequencies (15–307 kHz). …”
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Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais by Fernanda Oliveira de Sousa, Victor Andre Ariza Flores, Christhian Santana Cunha, Sandra Oda, Hostilio Xavier Ratton Neto

“…The models evaluated included linear regression, random forest, decision tree, and support vector machines. Among the evaluated models, Linear Regression emerged as the best-performing model with an R² value of 0.999998, a mean squared error (MSE) of 0.018672, and a low tendency to overfitting (0.000011).…”
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Machine Learning‐Based High‐Throughput Screening, Molecular Modeling and Quantum Chemical Analysis to Investigate Mycobacterium tuberculosis MetRS Inhibitors by Rajesh Maharjan, Kalpana Gyawali, Arjun Acharya, Madan Khanal, Kamal Khanal, Mohan Bahadur Kshetri, Dr. Madhav Prasad Ghimire, Dr. Tika Ram Lamichhane

“…This research aims to pinpoint the potential drug candidates targeting Mtb methionyl‐tRNA synthetase (MtbMetRS) using in silico techniques. Employing machine learning algorithms, including Random Forest, Extra Trees, and Nu‐Support Vector, a voting classifier was built to screen 10 million molecules. …”
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Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea by Sooyeon Yi, G. Mathias Kondolf, Samuel Sandoval Solis, Larry Dale

“…The study utilizes observed data that simulates conditions with and without the weir, including scenarios of full gate opening. Multiple machine learning algorithms—Random Forest (RF), Artificial Neural Network, Support Vector Regression (SVR), Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—are used to develop accurate groundwater level prediction models. …”
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Development and validation of machine learning models for predicting acute kidney injury in acute-on-chronic liver failure: a multimodel comparative study by Jing Zhang, Shuxuan Tang, Jingyuan Liu, Ang Li

“…Six ML models were developed: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and extreme gradient boosting (XGBoost). …”
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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 Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review by Nikoletta-Anna Kapogianni, Angeliki Sideraki, Christos-Nikolaos Anagnostopoulos

“…Moreover, it highlights how different algorithms—such as Support Vector Machines (SVMs), Random Forests, Deep Neural Networks, and Boosting methods—perform across various physiological signals (e.g., HRV, EDA, skin temperature). …”
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Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network. by Yeping Shi, Yunbo Shi, Haodong Niu, Jinzhou Liu, Pengjiao Sun

“…In tests comparing the performance of the SSA-BPNN, support vector machine (SVM), and random forest (RF) models, the SSA-BPNN achieves a 99.1% classification accuracy, better than the SVM and RF models. …”
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Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems by Ali Basem, Hanaa Kadhim Abdulaali, As’ad Alizadeh, Pradeep Kumar Singh, Komal Parashar, Ali E. Anqi, Husam Rajab, Pancham Cajla, H. Maleki

“…The proposed strategy combines machine learning algorithms, including multilayer perceptron neural network (MLPNN), generalized additive model (GAM), Gaussian kernel regression (GKR), support vector machine (SVM), and Gaussian process regression (GPR) with artificial intelligence-based metaheuristic optimization algorithms (PSO and GA) to optimize their structural/training parameters. …”
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A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology by Rohit ANAND, Roaf Ahmad PARRAY, Indra MANI, Tapan Kumar KHURA, Harilal KUSHWAHA, Brij Bihari SHARMA, Susheel SARKAR, Samarth GODARA, Shideh MOJERLOU, Hasan MIRZAKHANINAFCHI

“…The spectral data sets were analyzed using decision tree and support vector machine (SVM) algorithms to identify the most accurate model for distinguishing diseased and healthy plants. …”
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