Determination of sexual dimorphism with CBCT images of the frontal sinus using a predictive formula and an artificial neural network by Julyana de Araújo OLIVEIRA, Natália Rogério BORELLA, Flávia Maria de Moraes RAMOS-PEREZ, Andrea dos Anjos PONTUAL, Maria Alice Andrade CALAZANS, Felipe Alberto Barbosa Simão FERREIRA, Francisco MADEIRO, Maria Luiza dos Anjos PONTUAL

“…The frontal sinus morphometric features from 800 CBCT scans were analyzed using Mann-Whitney tests and a multivariate logistic regression model to identify key morphometric features for sex determination and to develop the predictive formula. …”
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Well Production Forecasting in Volve Field Using Kolmogorov–Arnold Networks by Xingyu Lu, Jing Cao, Jian Zou

“…However, traditional methods often struggle to capture the complex dynamics of reservoirs, and existing machine learning models rely on large parameter sets, resulting in high computational costs and limited scalability. …”
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Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study by Si Chen, Rui Nie, Xiaoran Shen, Yan Wang, Haixia Luan, Xiaoli Zeng, Yanhua Chen, Hui Yuan

“…Additionally, RDW levels were found to modestly mediate the relationship between age (per 10-year increase) and 180-day or 1-year mortality in GCA patients hospitalized or admitted to the ICU. The results of the machine learning analysis indicated that the model built using the random forest algorithm performed the best, with an area under the curve of 0.879. …”
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Breast cancer survival prediction using an automated mitosis detection pipeline by Nikolas Stathonikos, Marc Aubreville, Sjoerd deVries, Frauke Wilm, Christof A Bertram, Mitko Veta, Paul J vanDiest

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Significant associations between high-risk sexual behaviors and enterotypes of gut microbiome in HIV-negative men who have sex with men by Kangjie Li, Xinjing Liu, Xiaohua Zhong, Haijiao Zeng, Tian Liu, Bing Lin, Pinyi Chen, Biao Xie, Xiaoni Zhong

“…A three-category random forest machine learning model was performed to further examine the correlation between abundant microbiome in each enterotype cluster and anal sex roles. …”
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Development of a prognostic nomogram for ocular melanoma: a SEER population-based study (2000–2021) by Miyun Zheng, Miyun Zheng, Maodong Xu, Maodong Xu, Mengxing You, Mengxing You, Zhiqing Huang, Zhiqing Huang

“…This study develops a prognostic nomogram for OM using machine learning and internal validation techniques, aiming to improve prognosis prediction and clinical decision-making.MethodsIndependent prognostic variables were identified using univariate and multivariate COX proportional hazard regression models. …”
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A manganese metabolism-related gene signature stratifies prognosis and immunotherapy efficacy in kidney cancer by Yang Liu, Hao Ye, Ruoxuan Zhang, Xiaolong Liu, Ranlu Liu

“…The Ward.D2 method was used to identify MMCG subtypes, while Lasso-cox regression analysis was performed to establish the MMCG risk model. The predictive performance was validated through time-dependent ROC analysis, calibration curves, and decision curve analysis. …”
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Risk factors and predictive model construction for lower extremity arterial disease in diabetic patients. by Yingjie Kuang, Zhixin Cheng, Jun Zhang, Chunxu Yang, Yue Zhang

“…Logistic regression analysis was employed to identify related factors, and machine learning algorithms were used to construct the risk prediction model.…”
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Prediction Models for Postoperative Delirium of Cardiovascular Surgery (PODOCVS): Protocol for a Systematic Review by Xuling Zhao, Yike Wang, Liju Li, Meijuan Lan, Xiaodi He

“…Developing multivariable prediction models for stratifying PODOCVS risk would enable early, personalized interventions. …”
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Comparative evaluation of hybrid and individual models for predicting soybean yellow mosaic virus incidence by Yunish Khan, Vinod Kumar, Amel Gacem, Anurag Satpathi, Parul Setiya, Kumari Surbhi, Ajeet Singh Nain, Dinesh Kumar Vishwakarma, Ahmad J. Obaidullah, Krishna Kumar Yadav, Ozgur Kisi

“…Abstract Forecasting the severity of crop diseases is crucial for agricultural productivity and can be achieved through statistical and machine learning techniques. Predictive models that consider weather conditions during critical growth stages of crops have shown promising accuracy. …”
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Multi-view fusion of diffusion MRI microstructural models: a preterm birth study by Rosella Trò, Monica Roascio, Domenico Tortora, Mariasavina Severino, Andrea Rossi, Andrea Rossi, Eleftherios Garyfallidis, Gabriele Arnulfo, Gabriele Arnulfo, Marco Massimo Fato, Shreyas Fadnavis

“…Furthermore, we investigated discriminative patterns of preterm birth using multiple analysis methods, drawn from two only seemingly divergent modeling goals, namely inference and prediction. We thus resorted to (i) a traditional univariate voxel-wise inferential method, as the Tract-Based Spatial Statistics (TBSS) approach; (ii) a univariate predictive approach, as the Support Vector Machine (SVM) classification; and (iii) a multivariate predictive Canonical Correlation Analysis (CCA).Main resultsThe TBSS analysis revealed significant differences between preterm and term cohorts in several white matter areas for multiple HARDI features. …”
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Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA seq... by Yuqing Li, Jiandong Zhang, Xuemeng Qiu, Yifei Zhang, Jiyue Wu, Qing Bi, Zejia Sun, Wei Wang

“…Subsequently, weighted gene co-expression network analysis and seven machine learning algorithms were employed to identify RCD-related hub genes for renal fibrosis. …”
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Developing a prediction model for cognitive impairment in older adults following critical illness by Ashley E. Eisner, Lauren Witek, Nicholas M. Pajewski, Stephanie P. Taylor, Richa Bundy, Jeff D. Williamson, Byron C. Jaeger, Jessica A. Palakshappa

“…Patients were included in the cohort if they were admitted to the ICU for ≥ 48 h with ≥ 2 ambulatory visits prior to hospitalization and at least one visit in the post-discharge year. We used a machine learning model, oblique random survival forests (ORSF), to examine the multivariable association of 54 structured data elements available by 3 months after discharge with incident diagnoses of cognitive impairment or dementia over 1-year. …”
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Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis by Man Sun, Jianzhang Wang, Ping Xu, Libo Zhu, Gen Zou, Shuyi Chen, Yuanmeng Liu, Xinmei Zhang

“…Random forest algorithm was used to select the key radiomics features of different symptoms and develop the radiomic model by support vector machine algorithm. Multivariable logistic regression assessed clinical characteristics. …”
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Predicting grade II-IV bone marrow suppression in patients with cervical cancer based on radiomics and dosiomics by Yanchun Tang, Yanchun Tang, Yaru Pang, Jingyi Tang, Jingyi Tang, Xinchen Sun, Xinchen Sun, Peipei Wang, Jinkai Li

“…Clinical predictors were identified through both univariate and multivariate logistic regression analysis. Predictive models were constructed by intergrating clinical predictors with DVH parameters, combining DVH parameters and R-score with clinical predictors, and amalgamating clinical predictors with both D-score and R-score. …”
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Regional soil salinity analysis using stepwise M5 decision tree by Khalil Ghorbani, Soraya Bandak, Laleh Rezaei Ghaleh, Saeed Mehri, Aynaz Lotfata

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High dietary live microbe intake associated with reduced depressive symptoms in gastrointestinal disease patients: findings from a cross-sectional study by Cheng Zhang, Ming-Hui Ma, Ke Li, Shu-Ning Xu, Ying Liu

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U-shaped association between serum chloride and hypertension risk with nadir around 103 mmol/L: insights from regression and interpretable machine learning (XGBoost/SHAP) using NHA... by Shancheng He, Xuemei Zhong, Guangming Chen, Long Li

“…Additionally, to further explore the complex relationship between serum chloride levels and hypertension risk, and to understand the contributions of various features within a high-performance machine learning model, we trained an XGBoost classifier to predict hypertension status and utilized SHAP (SHapley Additive exPlanations) values for interpretation.ResultsA substantial connection was acquired between serum chloride levels and the risk of hypertension. …”
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A Data Imputation Strategy to Enhance Online Game Churn Prediction, Considering Non-Login Periods by JaeHong Lee, Pavinee Rerkjirattikal, SangGyu Nam

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Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease by Hanyu Luo, Benlong Wang, Rui Cao, Jun Feng

“…Lasso regression and multivariate logistic regression were used to compare the predictive value of these models. …”
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