Dataset and machine learning-based computer-aided tools for modeling working sorption isotherms in dried parchment and green coffee beansMendeley Data by Gentil A. Collazos-Escobar, Andrés F. Bahamón-Monje, Nelson Gutiérrez-Guzmán

“…Thereby, the MATLAB scripts implement an automated routine for the calibration and optimization of the Support Vector Machine (SVM) and Random Forest (RF) techniques, enabling the modeling of working sorption isotherms for each coffee type (considering only aw and temperature) and in a multivariate approach (incorporating aw, temperature, and coffee type) to predict the equilibrium moisture content (Xe). …”
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FRID-PI: a machine learning model for diagnosing fracture-related infections based on 18F-FDG PET/CT and inflammatory markers by Mei Yang, Quanhui Tan, Tingting Li, Jie Chen, Weiwei Hu, Yi Zhang, Xiaohua Chen, Jiangfeng Wang, Chentian Shen, Zhenghao Tang

“…In the training cohort, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model analysis and multivariate Cox regression analysis were utilized to identify predictive factors for FRI. …”
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Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. by Britta Trautwein, Meinrad Beer, Manfred Blobner, Bettina Jungwirth, Simone Maria Kagerbauer, Michael Götz

“…<h4>Methods</h4>This clinical cohort study will follow the TRIPOD statement for multivariable prediction model development. Development of the prognostic model will require 512 patients undergoing elective surgery under general anaesthesia. …”
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Prediction of 5-year postoperative survival and analysis of key prognostic factors in stage III colorectal cancer patients using novel machine learning algorithms by Wei Zhang, Yan Li, Jinghan Jia, Yuhang Yang, Yuyuan Hu, Yanhong Wang, Jinxi Wang

“…These factors were incorporated into machine learning models, including logistic regression, decision tree, LightGBM, and others. …”
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Integrative analysis of gene expression and DNA methylation through one‐class logistic regression machine learning identifies stemness features in medulloblastoma by Hao Lian, Yi‐Peng Han, Yu‐Chao Zhang, Yang Zhao, Shan Yan, Qi‐Feng Li, Bao‐Cheng Wang, Jia‐Jia Wang, Wei Meng, Jian Yang, Qin‐Hua Wang, Wei‐Wei Mao, Jie Ma

“…In addition, by combining the Lasso‐penalized Cox regression machine‐learning approach with univariate and multivariate Cox regression analyses, we identified a stemness‐related gene expression signature that accurately predicted survival in patients with Sonic hedgehog (SHH) MB. …”
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Diagnostic model of microvasculature and neurologic alterations in the retina and optic disc for lupus nephritis by Yun Yu, Xia-fei Pan, Qi-hang Zhou, Xiao-yin Zhou, Qian-hua Li, Yu-qing Lan, Xin Wen

“…Independent risk factors were identified through univariate and multivariate analyses, followed by the development of a random forest (RF) diagnostic model. …”
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Development and validation of prediction models for death within 6 months after cardiac arrest by Jianping Lu, Jianping Lu, Yuqi Zeng, Yuqi Zeng, Nan Lin, Qinyong Ye, Qinyong Ye

“…A risk prediction model was constructed using random forest methods, support vector machine (SVM), and a nomogram based on factors with P &lt; 0.1 in the multivariate logistic analyses. …”
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Enhancement of the Au&#x002F;ZnO-NA Plasmonic SERS Signal Using Principal Component Analysis as a Machine Learning Approach by Akhilesh Kumar Gupta, Chih-Hsien Hsu, Chao-Sung Lai

“…In this work, we modeled a novel approach to enhance surface-enhanced Raman scattering (SERS) signals using principal component analysis (PCA) as a machine learning approach. …”
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Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis by Liuhua Ke, Hongyu Zhang, Kang Long, Zheng Peng, Yongjun Huang, Xingxuan Ma, Wanjun Wu

“…A single-factor analysis (Model 1), Least Absolute Shrinkage and Selection Operator (LASSO) regression, and machine learning methods (Model 2) were used to screen important variables, and a multi-factor COX Proportional Hazards Model regression stroke recurrence risk prediction model was constructed. …”
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Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER‐Based Study by Xuanzi Li, Shuai Yang, Yingpeng Peng, Xueqiang You, Shunli Peng, Siyang Wang, Dasong Zha, Shuyuan Zhang, Chuntao Deng

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Development and validation of a prediction model for coronary heart disease risk in depressed patients aged 20 years and older using machine learning algorithms by Yicheng Wang, Yicheng Wang, Yicheng Wang, Chuan-Yang Wu, Hui-Xian Fu, Jian-Cheng Zhang, Jian-Cheng Zhang, Jian-Cheng Zhang

“…The validation set are used to evaluate the various performances of eight machine learning models. Several evaluation metrics were employed to assess and compare the performance of eight different machine learning models, aiming to identify the most effective algorithm for predicting coronary heart disease risk in individuals with depression. …”
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Prognostic effects of glycaemic variability on diastolic heart failure and type 2 diabetes mellitus: insights and 1-year mortality machine learning prediction model by Zhenkun Yang, Yuanjie Li, Yang Liu, Ziyi Zhong, Coleen Ditchfield, Taipu Guo, Mingjuan Yang, Yang Chen

“…This study examined the relationships between GV with mortality outcomes, and developed a machine learning (ML) model for long-term mortality in these patients. …”
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Interpretable machine learning model for identification and risk factor of premature rupture of membranes (PROM) and its association with nutritional inflammatory index: a retrospe... by Meng Zheng, Xiaowei Zhang, Haihong Wang, Ping Yuan, Qiulan Yu

“…This study aims to construct a risk factor prediction model related to PROM by using machine learning technology and explore the association with nutritional inflammatory index.MethodsA retrospective analysis was conducted on patients with PROM. …”
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Development and Validation of a Non-Invasive Prediction Model for Glioma-Associated Epilepsy: A Comparative Analysis of Nomogram and Decision Tree by Zhong Z, Yu HF, Tong Y, Li J

“…In addition, DCA analysis showed that in machine learning prediction models, decision trees have higher overall net returns within the threshold probability range.Conclusion: We have introduced a machine learning prediction model for GAE detection in glioma patients based on multiomics data. …”
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New Model for Weather Stations Integrated to Intelligent Meteorological Forecasts in Brasilia by Thomas Alexandre da Silva, Andre L. M. Serrano, Erick R. C. Figueiredo, Geraldo P. Rocha Filho, Fábio L. L. de Mendonça, Rodolfo I. Meneguette, Vinícius P. Gonçalves

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Development of a machine learning-based model to predict urethral recurrence following radical cystectomy: a multicentre retrospective study and updated meta-analysis by Bo Fan, Luxin Zhang, Hepeng Cui, Shanshan Bai, Haifeng Gao, Shengxiang Xiang, Yuchao Wang, Zhuwei Song, Jiaqiang Chen, Guanghai Yu, Jianbo Wang, Liang Wang, Zhiyu Liu

“…The best-performing model was selected based on these criteria. The SHapley Additive exPlanations (SHAP) method was used to calculate the contribution of each feature to the machine learning prediction with best performance and develop online calculator based on the machine model with the best performance. …”
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Multiparameter diagnostic model using S100A9, CCL5 and blood biomarkers for nasopharyngeal carcinoma by Lu Long, Ya Tao, Wenze Yu, Qizhuo Hou, Yunlai Liang, Kangkang Huang, Huidan Luo, Bin Yi

“…Variable selection was conducted using least absolute shrinkage and selection operator (LASSO) regression. NPC prediction models were developed using four machine-learning algorithms, and their performance was evaluated with ROC curves. …”
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A risk signature constructed by Tregs-related genes predict the clinical outcomes and immune therapeutic response in kidney cancer by Gang Li, Jingmin Cui, Tao Li, Wenhan Li, Peilin Chen

“…We further conducted the univariate Cox regression analysis and determined the prognosis-related KTRGs. Through the machine learning algorithm—Boruta, the potentially important KTRGs were screened further and submitted to construct a risk model. …”
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Intelligent multi-modeling reveals biological relationships and adaptive phenotypes for dairy cow adaptation to climate change by Robson Mateus Freitas Silveira, Angela Maria de Vasconcelos, Concepta McManus, Luiz Paulo Fávero, Iran José Oliveira da Silva

“…In this study, we develop a systematic methodology with multivariate models and machine learning algorithms to (i) model complex patterns of relationships or multi-phenotypic differences between the thermal environment and thermoregulatory, hormonal, biochemical, hematological and productive responses; and (ii) identify potential associations among biological relationships that may underlie shared and specific phenotypic patterns of adaptive responses. …”
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Interpreting Temporal Shifts in Global Annual Data Using Local Surrogate Models by Shou Nakano, Yang Liu

“…This paper focuses on explaining changes over time in globally sourced annual temporal data with the specific objective of identifying features in black-box models that contribute to these temporal shifts. Leveraging local explanations, a part of explainable machine learning/XAI, can yield explanations behind a country’s growth or downfall after making economic or social decisions. …”
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