Machine learning and the nomogram as the accurate tools for predicting postoperative malnutrition risk in esophageal cancer patients by Zhenmeng Lin, Zhenmeng Lin, Hao He, Mingfang Yan, Xiamei Chen, Hanshen Chen, Jianfang Ke

“…Feature selection was performed via the least absolute shrinkage and selection operator (LASSO) algorithm. Eight machine learning models were constructed and evaluated, alongside a nomogram developed through multivariable logistic regression.ResultsThe incidence of postoperative malnutrition was 45.4% (568/1,251) in the development cohort and 50.7% (224/442) in the validation cohort. …”
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Prognostic nutritional index and diabetic peripheral neuropathy in type 2 diabetes: a machine learning approach by Ya Wu, Danmeng Dong, Yang Liu, Xiaoyun Xie

“…Results Both RF and XGBoost models exhibited strong performance. The RF model achieved a recall of 78.4%, specificity of 87.8%, and accuracy of 84.0%, while the XGBoost model showed a recall of 77.4%, specificity of 92.1%, and accuracy of 84.8%. …”
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Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning by Weijun Zhou, Lijuan Li, Xiaowen Hao, Lanying Wu, Lifu Liu, Binyu Zheng, Yangzheng Xia, Yong Liu

“…ObjectiveTo develop and validate an interpretable machine learning (ML) model for the preoperative prediction of central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC).MethodsFrom December 2016 to December 2023, we retrospectively analyzed 710 PTMC patients who underwent thyroidectomies. …”
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A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

“…Objective: The purpose of this study is to explore the epidemiological characteristics of acute myeloid leukemia (AML) and establish a more accurate model for predicting the prognosis of AML patients based on machine learning. …”
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Probabilistic analysis of active earth pressures in spatially variable soils using machine learning and confidence intervals by Tran Vu-Hoang, Tan Nguyen, Jim Shiau, Duy Ly-Khuong, Hung-Thinh Pham-Tran

“…To improve computational efficiency and prediction accuracy, machine learning models, such as Multivariate Adaptive Regression Splines (MARS), are utilized to predict failure probabilities based on key spatial variability parameters. …”
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NOVEL KEY VARIABLES IN THE SURVIVAL OF PATIENTS WITH MYELODYSPLASTIC NEOPLASMS: A PRACTICAL APPROACH USING MACHINE LEARNING by PRC Passos, RDB Dias, SCC Carneiro, IB Nogueira, JMGF Lima, RC Venâncio, ACG Lavor, JVG Gama, RF Pinheiro, SMM Magalhães

“…Utilizing group elastic net machine learning, an artificial intelligence model capable of selecting relevant variables and assessing their discriminative power, we constructed 3 receiver operating characteristic (ROC) curves to predict 1, 3, and 5-year survival, extracting the area under the curve (AUC) and identifying variables with non-zero coefficients. …”
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A novel dynamic machine learning-based explainable fusion monitoring: application to industrial and chemical processes by Husnain Ali, Rizwan Safdar, Yuanqiang Zhou, Yuan Yao, Le Yao, Zheng Zhang, Weilong Ding, Furong Gao

“…This study presents a novel dynamic machine learning based explainable fusion approach to address the issues of process monitoring in industrial and chemical process systems. …”
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Basin dependencies of tropical cyclone genesis environment and possible future changes revealed by machine learning methods by QiFeng Qian, YeFeng Chen, XiaoJing Jia, Hao Ma, Wei Dong

“…Ocean basins are categorized into three groups based on PCA, and three MaxEnt machine learning (ML) models are developed to predict TC genesis under future scenarios. …”
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Investigating factors influencing quality of life in thyroid eye disease: insight from machine learning approaches by Haiyang Zhang, Shuo Wu, Lehan Yang, Chengjing Fan, Huifang Chen, Hui Wang, Tianyi Zhu, Yinwei Li, Jing Sun, Xuefei Song, Huifang Zhou, Terry J Smith, Xianqun Fan

“…For QOL-AP, gender (P = 0.013) and clinical activity (P = 0.086) were significant. The XGBoost model demonstrated superior performance, with an R2 of 0.872 and a root mean square error of 11.083. …”
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Rapid identification of tumor patients with PG-SGA ≥ 4 based on machine learning: a prospective study by Gui Qian, Huang Jiaxin, Cong minghua, Liu beijia, Li Yinfeng, Huang Guiyu, Yang Mingxue, Tang Xiaoli, Yan Hongyan

“…We confirmed the most important factors with logistic regression analysis. Results Among all models, XGBoost and Random Forest models perform the best, with the area under the curve (AUC) reaching of 0.75 and 0.77. …”
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Machine learning-based prediction of adverse pregnancy outcomes in antiphospholipid syndrome using pregnancy antibody levels by Wanqing Liu, Ju Huang, Jun Xiao, Shanling Yan

“…Six machine learning models were developed: Light Gradient Boosting Machine (LGBM), CatBoost, Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP). …”
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Multifactorial analysis of factors influencing elite australian football match outcomes: a machine learning approach by Fahey-Gilmour J., Dawson B., Peeling P., Heasman J., Rogalski B.

“…In Australian football (AF), few studies have assessed combinations of pre- game factors and their relation to game outcomes (win/loss) in multivariable analyses. Further, previous research has mostly been confined to association-based linear approaches and post-game prediction, with limited assessment of predictive machine learning (ML) models in a pre-game setting. …”
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The relationship between dietary vitamin B1 and stroke: a machine learning analysis of NHANES data by Shihan Guo, Shihan Guo, Xu Jiao, Xu Jiao, Mingfei Li, Zhuo Li, Yun Lu

“…Additionally, the Least Absolute Shrinkage and Selection Operator (LASSO) was utilized for feature selection. Eight machine learning methods were employed to construct predictive models and evaluate their performance. …”
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A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology by Yanni Wang, Wisit Cheungpasitporn, Hatem Ali, Jianbo Qing, Charat Thongprayoon, Wisit Kaewput, Karim M. Soliman, Zhengxing Huang, Min Yang, Zhongheng Zhang

“…Nonetheless, challenges including data quality, limited external validation, algorithmic bias, and poor interpretability constrain the clinical reliability of AI/ML models. To address these issues, this article offers a structured framework for nephrologist peer reviewers, integrating the TRIPOD-AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis–AI Extension) checklist. …”
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Optimization of urban green space in Wuhan based on machine learning algorithm from the perspective of healthy city by Xuechun Zhou, Xiaofei Zou, Wenzuixiong Xiong

“…Adopting a healthy city development perspective, the research aims to assess the impact of green space optimization on urban health, economic performance, and social structure.MethodsA multivariable model was constructed using random forest and Support Vector Machine (SVM) algorithms to evaluate the influence of key indicators on urban green space. …”
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Predictive analysis of root canal morphology in relation to root canal treatment failures: a retrospective study by Mohmed Isaqali Karobari, Vishnu Priya Veeraraghavan, P. J. Nagarathna, Sudhir Rama Varma, Jayaraj Kodangattil Narayanan, Santosh R. Patil

“…Additionally, machine learning algorithms were employed to develop a predictive model that was evaluated using receiver operating characteristic (ROC) curves.ResultsOf the 224 RCTs, 112 (50%) were classified as successful and 112 (50%) as failure. …”
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Justification of the Architecture a Promising Automated System for Monitoring Radiation, Chemical and Biological Environment Using Artificial Intelligence by S. A. Sharov, D. S. Batinov, M. A. Osipov, M. V. Domnin, S. A. Morozov, M. A. Golyshev, Yu. I. Khripkov, A. V. Nadein, I. V. Chebykin, V. D. Vasin, M. M. Bets

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Leveraging machine learning for duration of surgery prediction in knee and hip arthroplasty – a development and validation study by Benedikt Langenberger, Daniel Schrednitzki, Andreas Halder, Reinhard Busse, Christoph Pross

“…We therefore aimed (1) to identify whether machine learning can predict DOS in HA/KA patients using retrospective data available before surgery with reasonable performance, (2) to compare whether machine learning is able to outperform multivariable regression in predictive performance and (3) to identify the most important predictor variables for DOS both in a multi- and single-hospital context. …”
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Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure by Jacob C. Jentzer, Yogesh N.V. Reddy, Sabri Soussi, Ruben Crespo‐Diaz, Parag C. Patel, Patrick R. Lawler, Alexandre Mebazaa, Shannon M. Dunlay

“…In‐hospital mortality was evaluated using logistic regression, and 1 year mortality was evaluated using Cox proportional hazard models after multivariable adjustment. Results Among 4877 CICU patients with HF who had complete admission laboratory data (mean age 69.4 years, 38.4% females), we identified five clusters with divergent demographics, comorbidities, laboratory values, admission diagnoses and use of critical care therapies. …”
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A retrospective cohort study using machine learning to predict coronary artery lesions in children with Kawasaki disease by Yanan Duan, Aiping Chen, Xuedi Cheng

“…Subsequently, through machine learning, a predictive column chart model was constructed using clinical features and routine laboratory blood indicators, and the model was evaluated using ROC curves, calibration curves, and DCA curves. …”
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