Enhancement of standardized precipitation evapotranspiration index predictions by machine learning based on regression and soft computing for Iran's arid and hyper-arid region. by Saeid Bour, Zahra Kayhomayoon, Farhad Hassani, Sami Ghordoyee Milan, Ommolbanin Bazrafshan, Ronny Berndtsson

“…Initially, the standardized precipitation evapotranspiration index (SPEI) was calculated, and then using large-scale signals such as large-scale climate signals (the North Atlantic Oscillation, the Arctic Oscillation, the Pacific Decadal Oscillation, and the Southern Oscillation Index), along with climatic variables including temperature, precipitation, and potential evapotranspiration, predictions were made for the period of 1966-2014. Several new machine learning models including Least Square Support Vector Regression (LSSVR), Group Method of Data Handling (GMDH), and Multivariate Adaptive Regression Splines (MARS) were used for prediction. …”
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Application of machine learning methods on investigation the effect of interrupted deflector's height and downstream depth changes on energy dissipation at flip bucket spillways by Arash Adib, Danial Ebrahimzadeh, Mahmood S. Bejestan, Morteza Lotfirad, Masoud Oulapour

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Automated precision weighing: Leveraging 2D video feature analysis and machine learning for live body weight estimation of broiler chickens by Mairead Campbell, Paul Miller, Katerine Díaz-Chito, Sean Irvine, Mary Baxter, Jesús Martínez Del Rincón, Xin Hong, Niall McLaughlin, Thianantha Arumugam, Niamh O'Connell

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Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insights. by Jincan Li, Shengyue Wang

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Association of dietary quality, biological aging, progression and mortality of cardiovascular-kidney-metabolic syndrome: insights from mediation and machine learning approaches by Junfeng Ge, Lin Zhu, Sijie Jiang, Wenyan Li, Rongzhan Lin, Jun Wu, Fengying Dong, Jin Deng, Yi Lu

“…Furthermore, the Light Gradient Boosting Machine model showed strong performance in predicting advanced CKM staging (AUC: 0.896, 95% CI: 0.882–0.911), while Logistic regression performed better in predicting all-cause mortality (AUC: 0.857, 95% CI: 0.831–0.884). …”
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Machine learning consensus clustering for inflammatory subtype analysis in stroke and its impact on mortality risk: a study based on NHANES (1999–2018) by Zhang Chunjuan, Wang Yulong, Zhou Xicheng, Ma Xiaodong

“…NHANES 1999–2018. Weighted multivariate logistic regression was used to construct different models; consensus clustering methods were employed to subtype stroke patients based on inflammatory marker levels; LASSO regression analysis was used to construct an inflammatory risk score model to analyze the survival risks of different inflammatory subtypes; WQS regression, Cox regression, as well as XGBoost, random forest, and SVMRFE machine learning methods were used to screen hub markers which affected stroke prognosis; finally, a prognostic nomogram model based on hub inflammatory markers was constructed and evaluated using calibration and DCA curves.ResultsA total of 918 stroke patients with a median follow-up of 79 months and 369 deaths. …”
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Machine learning-based predictive tools and nomogram for in-hospital mortality in critically ill cancer patients: development and external validation using retrospective cohorts by Kaier Gu, Saisai Lu

“…The least absolute shrinkage and selection operator method was employed to screen predictive factors and construct six machine learning (ML) models. These models were mainly compared in terms of their predictive performance through area under the curve (AUC) and underwent external validation. …”
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Influence of race, ethnicity, and sex on the performance of epigenetic predictors of phenotypic traits by Dennis Khodasevich, Nicole Gladish, Saher Daredia, Anne K. Bozack, Hanyang Shen, Jamaji C. Nwanaji-Enwerem, Belinda L. Needham, David H. Rehkopf, Andres Cardenas

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Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis by Huili Zhao, Shenao Zhang, Lang Chen, Xin Liu, Aihong Cao, Peng Du

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Towards precision medicine strategies using plasma proteomic profiling for suspected gallbladder cancer: A pilot study by Ghada Nouairia, Martin Cornillet, Hannes Jansson, Annika Bergquist, Ernesto Sparrelid

“…Impact and implications: This study highlights the potential of plasma proteomic profiling to significantly improve the preoperative diagnostic accuracy of gallbladder cancer vs. cholecystitis. Using machine learning models, we identified biologically relevant plasma proteins associated with the diagnosis of gall bladder cancer. …”
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Association between accelerometer-measured physical activity volume and sleep duration in older adults: a cross-sectional interpretable machine learning analysis by XiaoTao Cai, Yi Xian, YuXin Zhou, TongYi Liu, Xinyue Zhang, Qing Chen

“…Analysis of the derivation cohort included weighted univariate analysis, weighted multivariate logistic regression, and interpretable machine learning analysis. …”
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Machine learning-based seismic response forecasting using feature mapping algorithms and scientometric analysis of nailed vertical excavation in a soil mass by Surya Muthukumar, Dhanya Sathyan, Premjith B, Sanjay Kumar Shukla

“…The research gap between the accuracy of observed and predicted values can be bridged by employing artificial intelligence-based machine learning (ML) models. The seismic displacement of the nailed soil wall obtained from experimental studies were assessed using suitable ML approaches. …”
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Enhancing early gestational diabetes mellitus prediction with imputation-based machine learning framework: A comparative study on real-world clinical records by Leyao Ma, Lin Yang, Yaxin Wang, Jie Hao, Yini Li, Liangkun Ma, Ziyang Wang, Ye Li, Suhan Zhang, Mingyue Hu, Jiao Li, Yin Sun

“…Conclusion This study demonstrates the critical role of imputation in improving the performance and fairness of GDM prediction models. The findings provide practical guidance for integrating imputation into clinical machine learning pipelines.…”
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Bayesian Computation Methods for Inference in Stochastic Kinetic Models by Eugenia Koblents, Inés P. Mariño, Joaquín Míguez

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Exploring the Potential of Enhanced Prognostic Performance of NCCN‐IPI in Diffuse Large B‐Cell Lymphoma by Integrating Tumor Microenvironment Markers: Stromal FOXC1 and Tumor pERK1... by Ji‐Ye Kim, Ibadullah Kahttana, Hyonok Yoon, Sunhee Chang, Sun Och Yoon

“…Multidimensional analysis using statistics and machine learning (ML) models assessed prognostic value of established clinicopathologic variables with stromal FOXC1 and tumor pERK1‐2 expressions. …”
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Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning by Shuxian Pan, Zibing Wang

“…Predictive models were constructed using three machine learning algorithms to analyze and statistically evaluate clinical characteristics, including immune cell data. …”
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Identification and optimization of relevant factors for chronic kidney disease in abdominal obesity patients by machine learning methods: insights from NHANES 2005–2018 by Xiangling Deng, Lifei Ma, Pin Li, Mengyang He, Ruyue Jin, Yuandong Tao, Hualin Cao, Hengyu Gao, Wenquan Zhou, Kuan Lu, Xiaoye Chen, Wenchao Li, Huixia Zhou

“…Furthermore, an optimal predictive model was developed for CKD using ten machine learning algorithms and enhanced model interpretability with the Shapley Additive Explanations (SHAP) method. …”
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An explainable web application based on machine learning for predicting fragility fracture in people living with HIV: data from Beijing Ditan Hospital, China by Bo Liu, Bo Liu, Qiang Zhang, Qiang Zhang, Xin Li, Xin Li

“…Six machine learning models (logistic regression, decision trees, SVM, KNN, random forest, and XGBoost) were trained with 10-fold cross-validation and hyperparameter tuning. …”
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Prediction model for the selection of patients with glioma to proton therapy by Jesper Folsted Kallehauge, Siri Grondahl, Camilla Skinnerup Byskov, Morten Høyer, Slavka Lukacova

“…The dataset was split into training (n = 37, period 2019–2022) and test (n = 12, period 2023) cohorts. Prediction models were built using logistic regression algorithms and support vector machines (SVMs) and evaluated using the area under the precision-recall curve (AUC-PR). …”
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Identification of potential diagnostic targets and therapeutic strategies for anoikis-related biomarkers in lung squamous cell carcinoma using machine learning and computational vi... by Xin Zhang, Jing Zou, Jinghua Ning, Yanhong Zhao, Run Qu, Yuzhe Zhang, Yuzhe Zhang, Yuzhe Zhang

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