Integrating ultrasound and clinical risk factors to predict carotid plaque vulnerability in gout patients: a machine learning approach by Yabin Fang, Yabin Fang, Kaiyi Yang, Kaiyi Yang, Xinyu Gao, Xinyu Gao, Yiran Gong, Yiran Gong, Yaxin Deng, Xiang Xu, Xiang Xu, Jing Xu, Jing Xu, Lei Yan, Lei Yan, Jinshu Zeng, Jinshu Zeng, Shuqiang Chen

“…The proposed machine learning model, integrating gout-specific and cardiovascular factors, provides a novel and effective approach for personalized risk stratification and management in gout patients, bridging the gap between rheumatic inflammation and cardiovascular risk assessment.…”
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Primary prevention cardiovascular disease risk prediction model for contemporary Chinese (1°P-CARDIAC): Model derivation and validation using a hybrid statistical and machine-learn... by Yekai Zhou, Celia Jiaxi Lin, Qiuyan Yu, Joseph Edgar Blais, Eric Yuk Fai Wan, Emmanuel Wong, Kathryn Tan, David Chung-Wah Siu, Kai Hang Yiu, Esther Wai Yin Chan, Doris Yu, William Wong, Tak-Wah Lam, Ian Chi Kei Wong, Ruibang Luo, Celine S L Chui

“…We employed XGBoost Cox model and multivariate imputation with chained equation (MICE) for derivation and missing data replacement. …”
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Microgrid Multivariate Load Forecasting Based on Weighted Visibility Graph: A Regional Airport Case Study by Georgios Vontzos, Vasileios Laitsos, Dimitrios Bargiotas, Athanasios Fevgas, Aspassia Daskalopulu, Lefteri H. Tsoukalas

“…The results indicate that the proposed method exhibits satisfactory performance relative to comparison models such as Exponential smoothing, ARIMA, Light Gradient Boosting Machine and CNN-LSTM. …”
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TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change by Juan Frausto Solís, Erick Estrada-Patiño, Mirna Ponce Flores, Juan Paulo Sánchez-Hernández, Guadalupe Castilla-Valdez, Javier González-Barbosa

“…This work presents TAE Predict (Time series Analysis and Ensemble-based Prediction with relevant feature selection) based on relevant feature selection and ensemble models of machine learning. Dimensionality in multivariate time series is reduced through Principal Component Analysis, ensuring interpretability and efficiency. …”
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Development and Validation of a Clinical Risk Model for Predicting Malignancy in Patients with Thyroid Nodules by Shiva Borzouei, Ali Safdari, Erfan Ayubi

“…The samples were patients referred to the specialized endocrinology clinic between 2014 and 2022. A multivariable model was built using demographic, clinical, and Bethesda System data through logistic regression as a generalized linear model (GLM). …”
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Inversion of Aerosol Chemical Composition in the Beijing–Tianjin–Hebei Region Using a Machine Learning Algorithm by Baojiang Li, Gang Cheng, Chunlin Shang, Ruirui Si, Zhenping Shao, Pu Zhang, Wenyu Zhang, Lingbin Kong

“…By comparing the inversion accuracies of single models—namely MLR (Multivariable Linear Regression) model, SVR (Support Vector Regression) model, RF (Random Forest) model, KNN (K-Nearest Neighbor) model, and LightGBM (Light Gradient Boosting Machine)—with that of the combined model (CM) after selecting the optimal model, we found that although the accuracy of the KNN model was the highest among the other single models, the accuracy of the CM model was higher. …”
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Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers by Rocco D’Ingillo, Alberto Castronovo, Stefano Straullu, Vittorio Curri

“…Future work will explore hybrid modeling approaches, integrating physics-based regression with Machine Learning (ML) to enhance performance in high-variance spectral regions. …”
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Building and explaining data-driven energy demand models for Indian states by Kieran M R Hunt, Hannah C Bloomfield

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Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning by Yang Chen, Zhengkun Yang, Yang Liu, Ying Gue, Ziyi Zhong, Tao Chen, Feifan Wang, Garry McDowell, Bi Huang, Gregory Y. H. Lip

“…Subsequently, GV and other clinical features were used to construct machine learning (ML) prediction models for 30-day all-cause mortality after ICU admission. …”
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Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle–Light Detection and Ranging and Machine Learning by Yan Yan, Jingjing Lei, Yuqing Huang

“…In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle–Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. …”
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A Comprehensive Model for Quantifying, Predicting, and Evaluating Ship Emissions in Port Areas Using Novel Metrics and Machine Learning Methods by Filip Bojić, Anita Gudelj, Rino Bošnjak

“…In the second module, the Multivariate Adaptive Regression Splines (MARS) machine learning method is adapted to predict emissions in varying operational scenarios. …”
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Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model by Yanling Zhang, Kun Long, Zhaojian Gong, Ruping Dai, Shuiting Zhang

“…Furthermore, Among the 6 machine learning models, logistic regression models performed best, with the higher AUC and accuracy. …”
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Clinical prediction model by machine learning to determine the results of maternal dietary avoidance in food protein-induced allergic proctocolitis infants by Jing Li, Meng-yao Zhou, Yang Li, Xue Wu, Xin Li, Xiao-li Xie, Li-jing Xiong

“…Variables were selected and incorporated into multiple machine learning models. Among them, the logistic regression model demonstrated relatively high stability and was ultimately selected for modeling. …”
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PCA- and PLSR-Based Machine Learning Model for Prediction of Urea-N Content in Heterogeneous Soils Using Near-Infrared Spectroscopy by Damiano Crescini, Gabriele Mascialino, Nicola Moggia, Giordano Piubeni, Mauro Serpelloni, Emilio Sardini

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Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…Machine learning models (AdaBoost, Random Forest, Gradient Boosting) were developed and evaluated utilizing these biomarkers to differentiate liver cancer. …”
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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

“…Our study attempted to investigate the independent predictors of overall survival (OS) and cancer-specific survival (CSS) in Asian patients with glioblastoma and establish predictive models for the OS and CSS of Asian patients with glioblastoma based on the machine learning algorithms. …”
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All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning by Min A, Liu Y, Fu M, Hou Z, Wang Z

“…By constructing a prognostic model based on machine learning, the risk factors of mortality in patients with intertrochanteric fractures and femoral neck fractures can be effectively identified, and personalized treatment strategies can be developed.Keywords: mortality, intertrochanteric fractures, femoral neck fractures, boruta algorithm, machine learning, prediction model…”
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Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment by Du S, Wu Y, Tao J, Shu L, Yan T, Xiao B, Lv S, Ye M, Gong Y, Zhu X, Hu P, Wu M

“…We randomly assigned these patients to training and validation cohorts with a ratio of 7:3. Univariate and multivariate logistic regressions were performed to find the potential factors, and then nine machine learning models and a stack ensemble model were developed with optimized variables. …”
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Evaluation of cotton planting suitability in Xinjiang based on climate change and soil fertility factors simulated by coupled machine learning model by Yonglin Jia, Yi Li, Asim Biswas, Jiayin Pang, Xiaoyan Song, Guang Yang, Zhen’an Hou, Honghai Luo, Xiangwen Xie, Javlonbek Ishchanov, Ji Chen, Juanli Ju, Kadambot H.M. Siddique

“…Xinjiang has seen an overall increase in cumulative temperature and rainfall, with southern Xinjiang showing the most significant rise (4.02% in temperature and 16.26% in rainfall). The random forest model (RF) outperformed multivariate linear regression (MLR) and support vector machines (SVM) in predicting soil fertility indicators (TN: R2=0.80, SOC: R2=0.77). …”
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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

“…After univariate and multivariate regression analyses, four ML models (Logistic Regression, XGBoost, Naive Bayes, and SVM) were constructed. …”
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