Construction and validation of a risk prediction model for chronic obstructive pulmonary disease (COPD): a cross-sectional study based on the NHANES database from 2009 to 2018 by Liqin Wang, Shijia Zhang, Zhaohong Gao, Deyou Jiang

“…Although several studies have explored the application of machine learning methods in COPD risk prediction, existing models often have limited feature dimensions and insufficient interpretability. …”
Get full text

Development and validation of a risk prediction model for acute kidney injury in coronary artery disease by Ming Ye, Chang Liu, Duo Yang, Hai Gao

Get full text

Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases by Fengwei Yao, Ji Luo, Qian Zhou, Luhua Wang, Zhijun He

“…Core risk factors were determined from the intersection of the three methods. A predictive model was constructed using multivariable logistic regression and visualized via a nomogram. …”
Get full text

CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph node metastasis in peripheral lung cancer by Xiaoyan Lu, Fan Liu, Jiahui E, Xiaoting Cai, Jingyi Yang, Xueqi Wang, Yuwei Zhang, Bingsheng Sun, Ying Liu

“…The aim of this study was to develop and validate a CT-based machine learning model integrating intra-and peri-tumoral features to predict OLNM in lung cancer patients. …”
Get full text

Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study by Jingchao Lei, Jia Zhai, Yao Zhang, Jing Qi, Chuanzheng Sun

“…MethodsThis retrospective multicenter cohort study adhered to the TRIPOD+AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Extended for Artificial Intelligence) guidelines. …”
Get full text

Construction of a novel online calculator for prediction of osteoporosis risk in Chinese type 2 diabetes patients by Xing Yu, Wenchi Liu, Xiaojun Chen, Yicheng Wang, Huibin Tang, Yunyun Su, Liangdi Xie, Li Luo

“…Machine learning(ML) models were developed to predict osteoporosis risk using different methods such as logistic regression (LR), naive Bayes (NB), neural network (NNET), support vector machine (SVM), gradient boosting machine (GBM), and k-nearest neighbor (KNN). …”
Get full text

Modeling and validation of wearable sensor-based gait parameters in Parkinson’s disease patients with cognitive impairment by Guo Hong, Guo Hong, Fengju Mao, Fengju Mao, Mingming Zhang, Fei Zhang, Fei Zhang, Xiangcheng Wang, Kang Ren, Kang Ren, Zhonglue Chen, Zhonglue Chen, Xiaoguang Luo, Xiaoguang Luo

“…The logistic regression model demonstrated superior predictive performance (test set AUC: 0.957), outperforming other machine learning algorithms. …”
Get full text

Prediction model of gastrointestinal tumor malignancy based on coagulation indicators such as TEG and neural networks by Fulong Yu, Chudi Sun, Liang Li, Xiaoyu Yu, Shumin Shen, Hao Qiang, Song Wang, Xianghua Li, Lin Zhang, Zhining Liu

“…This study builds various prediction models through machine learning methods based on the different coagulation statuses under varying malignancy levels of gastrointestinal tumors. …”
Get full text

Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study by Jiani Liu, Xin Zhang, Wei Li, Francis Manyori Bigambo, Dandan Wang, Xu Wang, Beibei Teng

“…Additionally, we evaluated the performance of the nine machine learning algorithms to determine the optimal model. …”
Get full text

A novel hybrid model for predicting the bearing capacity of piles by Li Tao, Xinhua Xue

“…The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. …”
Get full text

Prediction of bacteremia using routine hematological and metabolic parameters based on logistic regression and random forest models by Ting-Qiang Wang, Ying Zhuo, Chun-E Lv, Jing Shi, Ling-Hui Yao, Shi-Yan Zhang, Jinbao Shi

“…The area under the ROC curve (AUC) was 0.75 for the random forest model and 0.74 for logistic regression, with recall rates of 0.69 and 0.60, respectively.ConclusionRoutine laboratory markers integrated into machine learning models demonstrated potential for early bacteremia prediction. …”
Get full text

First nomogram for predicting interstitial lung disease and pulmonary arterial hypertension in SLE: a machine learning approach by Qian Niu, Qian Li, Shuaijun Chen, Lingyan Xiao, Jing Luo, Meng Wang, Linjie Song

“…Methods Using a retrospective cohort design, we analyzed 338 SLE patients (2007–2019), including 193 with ILD-PAH and 145 controls. Univariable and multivariable logistic regression identified independent predictors, followed by nomogram construction and random forest modeling. …”
Get full text

Non-invasive and early detection of tomato spotted wilt virus infection in tomato plants using a hand-held Raman spectrometer and machine learning modelling by Ciro Orecchio, Camilla Sacco Botto, Eugenio Alladio, Chiara D'Errico, Marco Vincenti, Emanuela Noris

“…The resulting condensed dataset was checked with multivariate exploratory methods and exploited to build multiple PLS-DA models, using different random splitting of the samples between training and test sets. …”
Get full text

Development of a postoperative recurrence prediction model for stage Ⅰ non-small cell lung cancer patients using multimodal data based on machine learning by ZHANG Di, WU Yi, XU Yu

“…Objective To develop a machine learning model integrating preoperative chest CT radiomic features with clinical data for predicting 5-year postoperative recurrence risk in stage Ⅰ non-small cell lung cancer (NSCLC) patients undergoing surgical resection. …”
Get full text

Dashboard‑Driven Machine Learning Analytics and Conceptual LLM Simulations for IIoT Education in Smart Steel Manufacturing by Mehdi Imani, Ali Imanifard, Babak Majidi, Abdolah Shamisa

“…Through advanced analytical models such as machine learning (ML) and, conceptually, Large Language Models (LLMs), this study explores how Industrial Internet of Things (IIoT) applications can transform educational experiences in the context of smart steel production. …”
Get full text

Optimizing water management and climate-resilient agriculture in rice-fallow regions of the Dwarakeswar river basin using ML models by Chiranjit Singha, Satiprasad Sahoo, Ajit Govind

“…This study analyzed soil moisture and GWL behavior in rice fallows along the Dwarakeswar River, India, using Sentinel-2, Landsat 8 OLI (2019–2022), and TerraClimate (1958–2022) datasets. Machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGB), and Multivariate Adaptive Regression Splines (MARS)—were applied to predict boro rice-fallows, soil moisture, and GWL. …”
Get full text

Multimodal data-driven prognostic model for predicting long-term outcomes in older adult patients with sarcopenia: a retrospective cohort study by Mengdie Liu, Wen Guo, Jin Peng, Jinhui Wu

“…Feature selection was performed using Lasso Regression, XGBoost, and Random Forest machine learning algorithms, and a nomogram model was developed using univariate and multivariate Cox regression analyses, with validation of its accuracy, concordance, and clinical applicability.ResultsA total of 12 feature variables were identified through the combined use of three machine learning methods. …”
Get full text

The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal valid... by Janny Xue Chen Ke, Arunachalam DhakshinaMurthy, Ronald B. George, Paula Branco

“…Abstract Purpose The availability of population datasets and machine learning techniques heralded a new era of sophisticated prediction models involving a large number of routinely collected variables. …”
Get full text

Screening risk factors for the occurrence of wedge effects in intramedullary nail fixation for intertrochanteric fractures in older people via machine learning and constructing a p... by Zhe Xu, Qiuhan Chen, Zhi Zhou, Jianbo Sun, Guang Tian, Chen Liu, Guangzhi Hou, Ruguo Zhang

“…Variables that appeared in the three machine learning methods were included in multivariate logistic regression to construct predictive models. …”
Get full text

Phenology-Based Maize and Soybean Yield Potential Prediction Using Machine Learning and Sentinel-2 Imagery Time-Series by Dorijan Radočaj, Ivan Plaščak, Mladen Jurišić

“…In this study, an effort was made to address the research gap concerning the effectiveness of phenological modeling in crop yield potential prediction using machine learning. …”
Get full text