Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses by Yahya Parvizi, Shahrokh Fatehi

“…The SOC changes were simulated using multivariate analysis and machine learning methods including generalized linear model (GLM), linear additive model (LAM), cubist, random forest (RF), and support vector machine (SVM) models. …”
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Relationships between vitamin C intake and COPD assessed by machine learning approaches from the NHANES (2017–2023) by Xinxin Tao, Xianwei Ye, Xianwei Ye

“…A weighted multivariate logistic regression model explored the VCI-COPD relationship. …”
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Prediction of summer precipitation via machine learning with key climate variables：A case study in Xinjiang, China by Chenzhi Ma, Junqiang Yao, Yinxue Mo, Guixiang Zhou, Yan Xu, Xuemin He

“…Study focus: This study aims to develop a machine learning model to predict summer precipitation (June–August) in XJ and explore the key variables contributing to summer precipitation in this region. …”
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Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning by Johan Helleberg, Anna Sundelin, Johan Mårtensson, Olav Rooyackers, Ragnar Thobaben

“…Training was performed using cross-validation in the training set, with forward stepwise feature selection and Bayesian hyperparameter optimization, and accuracy was assessed using area under the precision recall curve (AUCPR) in the test set. The best model was compared to a multivariate logistic regression model (LR) in the holdout set. …”
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Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai by V. A. Nevzorova, T. A. Brodskaya, K. I. Shakhgeldyan, B. I. Geltser, V. V. Kosterin, L. G. Priseko

“…The χ2, Fisher and MannWhitney tests, univariate logistic regression (LR) were used for data processing and analysis. To build predictive models, we used following machine learning (ML) methods: multivariate LR, Weibull regression, and stochastic gradient boosting.Results. …”
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Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations by Yong Li, Xin Sun, Yongtao Qu, Shuling Yang, Yueyi Zhai, Yan Qu

“…Machine learning algorithms like Logistic Regression and Random Forest were developed, optimized, and evaluated. …”
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Residential Building Renovation Considering Energy, Carbon Emissions, and Cost: An Approach Integrating Machine Learning and Evolutionary Generation by Rudai Shan, Wanyu Lai, Huan Tang, Xiangyu Leng, Wei Gu

“…To enhance the robustness of the methodology, a comparative analysis of four different ML models—light gradient boosting machine (LightGBM), fast random forest (FRF), multivariate linear regression (MVLR), and artificial neural network (ANN)—was conducted, with LightGBM demonstrating the best performance in terms of accuracy, adaptability, and efficiency. …”
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Lipoproteins predicting coronary lesion complexity in premature coronary artery disease: a supervised machine learning approach by Marta Marcinkowska, Agnieszka Kuchta, Petra Małgorzata Grešner, Tomasz Figatowski, Piotr Kasprzyk, Radosław Targoński, Wojciech Sobiczewski, Miłosz Jaguszewski, Marcin Fijałkowski, Marcin Gruchała, Agnieszka Mickiewicz

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A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles by Yanping Hu, Bo Wang, Chao Shi, Pengfei Ren, Chengjuan Zhang, Zhizhong Wang, Jiuzhou Zhao, Jiawen Zheng, Tingjie Wang, Bing Wei, He Zhang, Rentao Yu, Yihang Shen, Jie Ma, Yongjun Guo

“…Kaplan–Meier’s analysis showed that C1 and C2 were associated with a better DFS than C3 in some GC patient subgroups.Conclusions The machine learning model developed was found to be effective model at predicting the prognosis of patients with GC and their TIIC profiles for risk stratification in clinical settings.…”
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Skin hyperspectral imaging and machine learning to accurately predict the muscular poly-unsaturated fatty acids contents in fish by Yi-Ming Cao, Yan Zhang, Qi Wang, Ran Zhao, Mingxi Hou, Shuang-Ting Yu, Kai-Kuo Wang, Ying-Jie Chen, Xiao-Qing Sun, Shijing Liu, Jiong-Tang Li

“…In this study, we combined skin hyperspectral imaging (HSI) and machine learning (ML) methods to assess the muscular PUFAs contents of common carp. …”
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Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review by Rita Banzi, Jacques Demotes, Paula Garcia, Enrico Glaab, Armin Rauschenberger, Chiara Gerardi

“…These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation.Conclusions While most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. …”
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Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic rhinosinusitis with nasal polyps by Zhaonan Xu, Qing Hao, Bingrui Yan, Qiuying Li, Xuan Kan, Qin Wu, Hongtian Yi, Xianji Shen, Lingmei Qu, Peng Wang, Yanan Sun

“…The least absolute shrinkage and selection operator (LASSO) regression model and multivariate support vector machine recursive feature elimination (mSVM-RFE) were used to identify potential biomarkers, which were validated using the real time quantitative polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC). …”
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Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Appro... by Lanqian Yang, Jinmin Guo, Huili Tian, Min Liu, Chang Huang, Yang Cai

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Machine learning approaches for predicting frailty base on multimorbidities in US adults using NHANES data (1999–2018) by Teng Li, Xueke Li, Haoran XU, Yanyan Wang, Jingyu Ren, Shixiang Jing, Zichen Jin, Gang chen, Youyou Zhai, Zeyu Wu, Ge Zhang, Yuying Wang

“…And in machine learning process, feature selection for the frailty prediction model involved three algorithms. …”
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Low-carbohydrate diet score and chronic obstructive pulmonary disease: a machine learning analysis of NHANES data by Xin Zhang, Jipeng Mo, Kaiyu Yang, Tiewu Tan, Cuiping Zhao, Hui Qin

“…Additionally, we employed eight machine learning methods—Boost Tree, Decision Tree, Logistic Regression, MLP, Naive Bayes, KNN, Random Forest, and SVM RBF—to build predictive models and evaluate their performance. …”
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Whole-genome sequencing and machine learning reveal key drivers of delayed sputum conversion in rifampicin-resistant tuberculosis by Qing Fang, Xiangchen Li, Xiangchen Li, Yewei Lu, Junshun Gao, Yvette Wu, Yi Chen, Yang Che

“…Univariate analysis linked 2-month SCC failure to smear positivity, resistance to isoniazid, amikacin, capreomycin, and levofloxacin, and pre-XDR-TB status, though only smear positivity (aOR=2.41, P=0.008) and levofloxacin resistance (aOR=2.83, P=0.003) persisted as independent predictors in multivariable analysis. A Random Forest model achieved robust prediction of SCC failure (AUC: 0.86 ± 0.06 at 2 months; 0.76 ± 0.10 at 6 months), identifying levofloxacin resistance (feature importance: 6.37), embB_p.Met306Ile (5.94), and smear positivity (5.12) as top 2-month predictors, while katG_p.Ser315Thr (4.85) and gyrA_p.Asp94Gly (3.43) dominated 6-month predictions. …”
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Machine-learning approaches to identify determining factors of happiness during the COVID-19 pandemic: retrospective cohort study by Takahiro Tabuchi, Yusuke Tsugawa, Tadahiro Goto, Itsuki Osawa, Hayami K Koga

“…We defined participants with ≥8 on the scale as having high levels of happiness.Results Among the 25 482 respondents, the median score of self-reported happiness was 7 (IQR 6–8), with 11 418 (45%) reporting high levels of happiness during the pandemic. The multivariable logistic regression model showed that meaning in life, having a spouse, trust in neighbours and female gender were positively associated with happiness (eg, adjusted OR (aOR) for meaning in life 4.17; 95% CI 3.92 to 4.43; p<0.001). …”
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Radiomics-Based Machine Learning for Determining Amplification Status in Childhood Neuroblastoma: A Systematic Review and Meta-Analysis by Haoru Wang MD, Yi Ji MD, Xin Chen MD, Ling He MD, Xiangming Fang MD, Jinhua Cai MD

“…A meta-analysis of validation performance was performed on studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement Type 2a or higher. …”
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Dietary antioxidants and flavonoids are inversely associated with prostate cancer risk and mortality: evidence from NHANES and machine learning by Yanyang Jin, Dong-shan Lv, Li-po Zhou, Li Xiao, Guang-quan Tong, Abdallah Karim, Shuai Xue, Hongyang Tian, Cheng-cai Wang, Kun Feng, Ding-ming Song, You-liang Guan

“…Nine supervised machine learning models, including random forest (RF), were developed and validated. …”
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A robust multi-model framework for groundwater level prediction: The BFSA-MVMD-GRU-RVM model by Akram Seifi, Sharareh Pourebrahim, Mohammad Ehteram, Hanieh Shabanian

“…This study introduces a novel model combining multivariate variational mode decomposition (MVMD), gated recurrent unit (GRU), and relevance vector machine (RVM), along with the Boruta feature selection algorithm (BFSA), for precise groundwater level forecasting. …”
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