Development and Validation of a Nomogram Prediction Model for In-hospital Mortality in Patients with Cardiac Arrest: A Retrospective Study by Peifeng Ni, Shurui Xu, Weidong Zhang, Chenxi Wu, Gensheng Zhang, Qiao Gu, Xin Hu, Ying Zhu, Wei Hu, Mengyuan Diao

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A multi-algorithm prognostic model combining inflammatory indices and surgical features in distal cholangiocarcinoma by Yi Yin, Yi Yin, Luyuan Bai, Xinyue Mu, Xinyue Mu, Shan Zhang, Panpan Zhai, Panpan Zhai

“…Patients stratified into a low-dNLR group (≤ 1.60) demonstrated significantly improved recurrence-free survival (41 months) and overall survival (17 months) compared to those in the high-dNLR group (> 1.60) (p < 0.05). Univariate and multivariate combined with 3 machine learning analyses identified preoperative dNLR > 1.60 as an independent adverse prognostic factor for postoperative outcomes, incorporating with other independent predictors (preoperative total bilirubin, carbohydrate antigen 19–9 levels, T-stage, portal venous system invasion, and lymph node metastasis) further enhanced the predictive accuracy of the prognostic model.ConclusionA preoperative dNLR > 1.60 is an independent risk factor associated with poor prognosis in patients with dCCA. …”
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MRI feature-based discrimination model for prediction of MGMT promoter methylation status in glioma by ZHANG Zhi-zhong, YOU Na, LIU Ming-hang, LI Ze, SUN Guo-chen, ZHAO Kai

“…For the prediction task, further train and test 4 machine learning (ML) models, namely Logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting (GB). …”
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Association of sarcopenia with all-cause and cause-specific mortality in cancer patients: development and validation of a 3-year and 5-year survival prediction model by Feng Cui, Xiangji Dang, Daiyun Peng, Yuanhua She, Yubin Wang, Ruifeng Yang, Zhiyao Han, Yan Liu, Hanteng Yang

“…Furthermore, we plan to develop risk prediction models using machine learning algorithms to predict 3-year and 5-year survival rates in cancer patients. …”
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Extraction and Spatiotemporal Analysis of Rubber Plantations on Hainan Island, China (1990–2023) Using Phenological and Multivariate Remote Sensing Features by Donghua Wang, Huichun Ye, Yanan You, Chaojia Nie, Jingjing Wang, Jingjuan Liao, Bingsun Wu, Lixia Shen, Dailiang Peng

“…Remote sensing combined with machine learning offers a scalable solution, though research integrating phenological traits and multivariate data remains limited. …”
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Development and validation of a machine learning model for central compartmental lymph node metastasis in solitary papillary thyroid microcarcinoma via ultrasound imaging features... by Haiyang Han, Heng Sun, Chang Zhou, Li Wei, Liang Xu, Dian Shen, Wenshu Hu

“…Conclusion A machine learning-based model combining ultrasound radiomics and clinical variables shows promise for the preoperative risk stratification of CCLNM in patients with PTMC. …”
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A novel machine learning-assisted clinical diagnosis support model for early identification of pancreatic injuries in patients with blunt abdominal trauma: a cross-national study by Sai Huang, Xuan Zhang, Bo Yang, Yue Teng, Li Mao, Lili Wang, Jing Wang, Xuan Zhou, Li Chen, Yuan Yao, Cong Feng

“…The aim of this study was to develop a machine learning model to support clinical diagnosis for early detection of abdominal trauma. …”
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Potential effects of endocrine-disrupting chemicals on preserved ratio impaired spirometry revealed by five different approaches by Chenyuan Deng, Yu Jiang, Yuechun Lin, Hengrui Liang, Wei Wang, Ying Huang, Jianxing He

“…The mixed effects of multiple EDCs on PRISm were assessed using three mixture analysis models: weighted quantile sum (WQS) regression, quantile g-computation (Qgcomp), and Bayesian kernel machine regression (BKMR). …”
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Machine Learning Methods in the Comparative Evaluation of Various Approaches to the Surgical Treatment of Primary Angle Closure by N. I. Kurysheva, A. L. Pomerantsev, O. Ye. Rodionova, G. A. Sharova

“…The specificity for the control group was 100 %, and this group located far from the target group.Conclusion. Machine learning methods make it possible to compare groups with multivariate and correlated parameters. …”
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Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES by Ningyi Cheng, Yukun Chen, Lei Jin, Liangwan Chen

“…Methods After excluding missing data, 30,704 participants were analyzed via survey-weighted statistics, restricted cubic splines (RCS), stratified analyses, and multivariate logistic regression. Ensemble models were compared for HF classification, with SHAP quantifying feature importance. …”
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Application of prediction model based on CT radiomics in prognosis of patients with non-small cell lung cancer by Zefei Peng, Yubo Wang, Yurong Qi, Hao Hu, Yang Fu, Jiageng Li, Wei Li, Zhanxuan Li, Weilian Guo, Chunqi Shen, Jiezhi Jiang, Bin Yang

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A novel early stage drip irrigation system cost estimation model based on management and environmental variables by Masoud Pourgholam-Amiji, Khaled Ahmadaali, Abdolmajid Liaghat

“…The selection of features was carried out for all features (a total of 39 features) as well as for easily available features (those features that existed before the irrigation system’s design phase, 18 features). Then, different machine learning models such as Multivariate Linear Regression, Support Vector Regression, Artificial Neural Networks, Gene Expression Programming, Genetic Algorithms, Deep Learning, and Decision Trees, were used to estimate the costs of each of the of the aforementioned sections. …”
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Application Value of an AI-based Imaging Feature Parameter Model 
for Predicting the Malignancy of Part-solid Pulmonary Nodule by Mingzhi LIN, Yiming HUI, Bin LI, Peilin ZHAO, Zhizhong ZHENG, Zhuowen YANG, Zhipeng SU, Yuqi MENG, Tieniu SONG

“…Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). …”
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Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based cal... by Yan Zhang, Nan Wang, Yuxin Qiu, Yingxiao Jiang, Peiyan Qin, Xiaoxiao Wang, Yang Li, Xiangdi Meng, Furong Hao

“…The study aimed to identify predictors of LNM and to develop a machine learning (ML)-based risk prediction model for patients with breast IMPC. …”
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A predictive model to identify optimal candidates for surgery among patients with metastatic colorectal cancer by Xiqiang Zhang, Zhaoyi Jing, Longchao Wu, Ze Tao, Dandan Lu

“…After PSM, compared to no surgical intervention, surgical intervention was independently associated with an extended median CSS [median: 22 vs. 12 months; HR: 2.323, P < 0.001]. Among the nine machine learning models, the Categorical Boosting model performed the best but was still slightly inferior to traditional logistic regression. …”
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Associations of urinary caffeine metabolites with sex hormones: comparison of three statistical models by Jianli Zhou, Linyuan Qin, Linyuan Qin

“…We also fitted weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) methods to further assess these relationships.ResultsIn the PCA-multivariable linear regression, PC2 negatively correlates with E2: β = −0.01, p-value = 0.049 (male population). …”
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A predictive model for early neurological deterioration after intravenous thrombolysis in patients with ischemic stroke by Liping He, Liping He, Meng Zhang, Meng Zhang, Fei Xu, Zhangsong Wu, Huijuan Chen, Ying Li, Ran Chen

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A combined prediction model for middle and long-term water and sediment forecasting based on causal analysis feature selection by XuanZhao Kong, KeBin Shi, HaiKun Yao, SiHai Liu

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Cloud based real-time multivariate multi-step prediction of systolic blood pressure and heart rate using temporal convolutional network and Apache Spark by Hager Saleh, Nora El-Rashidy, Sherif Mostafa, Abdulaziz AlMohimeed, Shaker El-Sappagh, Zainab H. Ali

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Unraveling volatile metabolites in pigmented onion (Allium cepa L.) bulbs through HS-SPME/GC–MS-based metabolomics and machine learning by Kaiqi Cheng, Kaiqi Cheng, Jingzhe Xiao, Jingyuan He, Rongguang Yang, Jinjin Pei, Jinjin Pei, Wengang Jin, Wengang Jin, A. M. Abd El-Aty, A. M. Abd El-Aty

“…Volatile metabolites were identified using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS). Multivariate statistical analyses, feature selection techniques (SelectKBest, LASSO), and machine learning models were applied to further analyze and classify the metabolite profiles.ResultsSignificant differences in phytochemical composition and antioxidant activities were observed among the three onion types. …”
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