Developing a nomogram model for predicting non-obstructive azoospermia using machine learning techniques by Hong Xiao, Yi-lang Ding, Chao Wang, Peng Yang, Qiang Chen, Hao-nan He, Ruijie Yao, Hai-lin Huang, Xi Chen, Mao-yuan Wang, Song-xi Tang, Hui-liang Zhou

“…Following this, nine machine learning methods were employed to refine the prediction model. …”
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A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar by Saurabh Kumar, Uruya Weesakul, Divesh Ranjan Kumar, Pradeep Thangavel, Warit Wipulanusat, Jirapon Sunkpho

“…Abstract Corrosion can affect water taste, color, and odor, making it crucial to monitor and control corrosion in the water distribution network to maintain water quality standards. This study used machine learning approaches such as MARS, GMDH, and MPMR to model the corrosion rate in water distribution networks. …”
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Identification of progression-related genes and construction of prognostic model for chronic kidney disease by machine learning by Bingkun Zhou, Hu Zhou, Xiaodong Huang, Shijie Liu

“…In external validation, the maximal plage score had best classification performance for CKD (AUC:0.767) in GSE66494 and in GSE180394 (AUC:0.760), the medium plage score achieved a predictive performance for CKD progression (AUC = 0.758) in GSE45980. In the multivariate model, Cox regression analysis constructed a risk model with only minimal z-score, further LASSO regression analysis included gender and minimal z-score, but logistic regression multivariate analysis failed to be constructed with any score. …”
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Measurement and Modeling of Spindle Thermal Error of Fiveaxis CNC Machine Tool with Double Turntable by LIU Xianli, SONG Houwang, WU Shi, YUE Caixu, Steven Y.Liang, LI Rongyi

“…In order to measure the thermal error of the spindle in the actual cutting process of CNC machine tools and optimize the output of the thermal error model, a method of measuring the thermal error of the spindle of machine tools by using the thermal test piece is proposed, and the thermal error is separated by using the error characteristics. …”
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Hybridization of Machine Learning Algorithms and an Empirical Regression Model for Predicting Debris-Flow-Endangered Areas by Xiang Wang, Mi Tian, Qiang Qin, Jingwei Liang

“…Three commonly used machine-learning models (i.e., multivariate adaptive regression splines (MARS), random forest (RF), and support vector machine (SVM)) are developed based on the training datasets of a specific debris basin. …”
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Development and validation of a machine learning model for predicting pulmonary metastasis in hepatocellular carcinoma patients by Gangfeng Zhu, Qiang Yi, Rui Xu, Yi Xie, Siying Chen, Yipeng Song, Yi Xiang, Xiangcai Wang, Li Huang

“…Feature selection was conducted using the Boruta algorithm and multivariate logistic regression. Eight machine learning models were then developed and evaluated using validation cohorts for predictive performance. …”
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Machine Learning Models Decoding the Association Between Urinary Stone Diseases and Metabolic Urinary Profiles by Lin Ma, Yi Qiao, Runqiu Wang, Hualin Chen, Guanghua Liu, He Xiao, Ran Dai

“…<b>Background:</b> Employing advanced machine learning models, we aim to identify biomarkers for urolithiasis from 24-h metabolic urinary abnormalities and study their associations with urinary stone diseases. …”
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Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study by Juan Xie, Run-wei Ma, Yu-jing Feng, Yuan Qiao, Hong-yan Zhu, Xing-ping Tao, Wen-juan Chen, Cong-yun Liu, Tan Li, Kai Liu, Li-ming Cheng

“…Some studies have attempted to develop risk prediction models based on multivariate data, but their performance can be improved. …”
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A Clinical Risk Prediction Model for Depressive Disorders Based on Seven Machine Learning Algorithms by Jin W, Chen S, Wang M, Lin P

“…However, due to concerns about potential overfitting, the multivariable logistic regression model was selected as the final predictive model. …”
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Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (<i>Dzud</i>) in Mongolian Nomadic Pasturelands by Sinkyu Kang, Nanghyun Cho, Amartuvshin Narantsetseg, Bolor-Erdene Lkhamsuren, Otgon Khongorzul, Tumendemberel Tegshdelger, Bumsuk Seo, Keunchang Jang

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Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method by Tao Huang, Yuanyuan Li, Simin Wang, Shijie Qiao, Xiujuan Zheng, Wenhui Xiong, Menghan Yang, Xirui Huang, Bizhen Gao

“…However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. …”
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Construction of a predictive model for cognitive impairment among older adults in Northwest China by Yu Wang, Ni Wang, Yanjie Zhao, Xiaoyan Wang, Yuqin Nie, Liping Ding

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Establishment of two pathomic-based machine learning models to predict CLCA1 expression in colon adenocarcinoma. by Caiyun Yao, Maotong Hu, Lingxia Zhou, Hui Chen, Yang Cao

“…Two pathomics-based machine learning models were developed to predict CLCA1 expression from H&E stained images of COAD. …”
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An interpretable machine learning model for predicting myocardial injury in patients with high cervical spinal cord injury by Jiaqi Li, Jiaqi Li, Bingyu Zhang, Bingyu Zhang, Ye Liao, Ye Liao, Liqin Wei, Qinfeng Huang, Qinfeng Huang, Lijun Lin, Lijun Lin, Jiaxin Chen, Jiaxin Chen, Hui Chen, Hui Chen

“…Four machine learning (ML) models—logistic regression, gradient boosting machine (GBM), neural network (NeuralNetwork), and adaptive boosting (AdaBoost)—were constructed to predict myocardial injury, and model performance was evaluated using the area under the curve (AUC), F1 score, and average precision (AP). …”
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Prediction of Early Mortality in Esophageal Cancer Patients with Liver Metastasis Using Machine Learning Approaches by Yongxin Sheng, Liyuan Zhang, Zuhai Hu, Bin Peng

“…Prognostic factors were identified using univariate and multivariate logistic regression, and seven machine learning models, including extreme gradient boosting (XGBoost) and support vector machine (SVM), were developed to predict early mortality. …”
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Comparison of Machine Learning and Classic Methods on Aerodynamic Modeling and Control Law Design for a Pitching Airfoil by Lang Yan, Xinghua Chang, Nianhua Wang, Laiping Zhang, Wei Liu, Xiaogang Deng

“…Hence, this study focuses on how to construct the aerodynamic model and design control law using machine learning, as well as their differences from classical methods. …”
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Construction and validation of a machine learning based prognostic prediction model for children with traumatic brain injury by Yongwei Wei, Jiandong Wang, Yu Su, Fan Zhou, Huaili Wang

“…ObjectiveThis study aimed to establish a prediction model for the short-term prognosis of children with traumatic brain injury (TBI) using machine learning algorithms.MethodsThe clinical data of children with TBI who were treated in the First Affiliated Hospital of Zhengzhou University were retrospectively analyzed. …”
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Field scale wheat yield prediction using ensemble machine learning techniques by Sandeep Gawdiya, Dinesh Kumar, Bulbul Ahmed, Ramandeep Kumar Sharma, Pankaj Das, Manoj Choudhary, Mohamed A. Mattar

“…Big data framework was used to develop and refine several ensemble machine learning models based on field trial datasets. …”
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Hybrid Tree-Based Machine Learning Models for State-of-Charge and Core Temperature Estimation in EV Batteries by Aya Haraz, Khalid Abualsaud, Ahmed M. Massoud

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Machine learning-based identification of co-expressed genes in prostate cancer and CRPC and construction of prognostic models by Changhui Fan, Zhiheng Huang, Han Xu, Tianhe Zhang, Haiyang Wei, Junfeng Gao, Changbao Xu, Changhui Fan

“…Next, we applied LASSO regression, univariate, and multivariate COX regression analyses to pinpoint genes linked to prognosis and build prognostic models. …”
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