Construction and validation of a predictive model for lymph node metastasis in patients with papillary thyroid carcinoma by Yanhong Hao, Yanjing Zhang, Yuan Su, Liping Liu

“…The least absolute shrinkage and selection operator (LASSO) was used to select the features, and multiple logistic regression was used to analyze the predictive factors. Multiple machine learning (ML) classification models are integrated to analyze and identify the optimal model, while Shapley additive exPlanations (SHAPs) are used for personalized risk assessment. …”
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A Detailed Review for Predicting the Quantity of Sugar From Sugarcane Using Various Models by Kathirvel Narayanasamy, Ilayaraja Venkatachalam

“…Therefore, it is necessary to analyze sugarcane juice parameters and develop a machine-learning model based on these data. Enhanced machine learning techniques and comprehensive quality parameters have the potential to significantly enhance the precision of sugar content predictions.…”
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A Decısıon Model Based on Artıfıcıal Intellıgence for Dısease Predıctıon And Patıent Treatment Process Plannıng by Serkan Derici, Nuri Özgür Doğan

“…Methodology: A linear regression model was developed and applied to predict individuals at high risk of developing lung cancer using machine learning on the Microsoft Azure Machine Learning Studio platform. …”
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Predictive performance and uncertainty analysis of ensemble models in gully erosion susceptibility assessment by Congtan Liu, Haoming Fan, Yixuan Wang

“…This study aims to identify the optimal feature datasets and to quantify the uncertainty associated with gully erosion prediction models by developing a novel methodological framework based on ensembles of the three machine learning models: Random Forest (RF), Convolutional Neural Network (CNN), and Transformer models. …”
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Integrating Explainable Artificial Intelligence With Advanced Deep Learning Model for Crowd Density Estimation in Real-World Surveillance Systems by Sultan Refa Alotaibi, Hanan Abdullah Mengash, Mohammed Maray, Faiz Abdullah Alotaibi, Abdulwhab Alkharashi, Ahmad A. Alzahrani, Moneerah Alotaibi, Mrim M. Alnfiai

“…The system assists in detecting and analyzing crowd density in real-time by utilizing artificial intelligence and machine learning (ML) models on surveillance videos. …”
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Temperature-dependent compressive strength modeling of geopolymer blocks utilizing glass powder and steel slag by Supriya Janga, Ashwin Raut, Alireza Bahrami, T. Vamsi Nagaraju, Sridevi Bonthu

“…Employing machine learning techniques to predict the compressive strength of geopolymer blocks under various elevated temperature conditions improves predictive accuracy and optimizes resource utilization, leading to significant time savings.…”
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Predicting bone metastasis risk of colorectal tumors using radiomics and deep learning ViT model by Guanfeng Chen, Wenxi Liu, Yingmin Lin, Jie Zhang, Risheng Huang, Deqiu Ye, Jing Huang, Jieyun Chen

“…LASSO regression was applied to select key features, which were then used to build traditional machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, LightGBM, and XGBoost. …”
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Machine-learning models for differentiating benign and malignant breast masses: Integrating automated breast volume scanning intra-tumoral, peri-tumoral features, and clinical info... by Meixue Dai, Yueqiong Yan, Zhong Li, Jidong Xiao

“…However, the impact of peritumoral region size on predictive performance has not been systematically studied. This study aims to optimize diagnostic performance by integrating radiomics features and clinical data using multiple machine-learning models. …”
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Development and validation of an ultrasound-based interpretable machine learning model for the classification of ≤3 cm hepatocellular carcinoma: a multicentre retrospective diagnos... by Zhicheng Du, Fangying Fan, Jun Ma, Jing Liu, Xing Yan, Xuexue Chen, Yangfang Dong, Jiapeng Wu, Wenzhen Ding, Qinxian Zhao, Yuling Wang, Guojun Zhang, Jie Yu, Ping Liang

“…Summary: Background: Our study aimed to develop a machine learning (ML) model utilizing grayscale ultrasound (US) to distinguish ≤3 cm small hepatocellular carcinoma (sHCC) from non-HCC lesions. …”
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Development and validation of a novel predictive model for dementia risk in middle-aged and elderly depression individuals: a large and longitudinal machine learning cohort study by Xuan Xiao, Yihui Li, Qiaoboyang Wu, Xinting Liu, Xu Cao, Maiping Li, Jianjing Liu, Lianggeng Gong, Xi-jian Dai

“…The DeLong's test revealed no statistically significant difference in AUC values between models using 12 and 27 variables (p = 0.278). For practical implementation, we deployed the optimal model to a web application for visualization and dementia risk assessment, named DRP-Depression. …”
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Development of a novel constitutive model incorporating phase transformation and dynamic recrystallization effects for laser-assisted machining of Ti6Al4V alloy by Binbin Xu, Xin Liu, Hongguang Liu, Shijia Shi, Yuyang Tang, Jun Zhang

“…These results not only enhance our theoretical understanding of microstructural evolution under extreme conditions but also provide practical guidelines for optimizing machining parameters in high-performance manufacturing systems.…”
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Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal st... by Gege Zhang, Sijie Dong, Li Wang

“…This study aims to construct a machine learning model to predict the risk of depression in middle-aged and elderly patients with CMD and to identssify key risk factors. …”
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A Machine Learning–Based Prognostication Model Enhances Prediction of Early Hepatic Encephalopathy in Patients With Noncancer-Related Cirrhosis: Multicenter Longitudinal Cohort Stu... by Hsin-Yu Chen, Yiu-Hua Cheng, Wei-Chung Yeh, Yi-Chuan Chen, Yi-Wen Tsai

“…ObjectiveThis study aimed to develop a novel machine learning (ML) model to improve early prediction of HE in patients with noncancer-related cirrhosis. …”
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A personalized prediction model for distinguishing between asymptomatic bacteriuria and symptomatic urinary tract infections in patients with type 2 diabetes mellitus using machine... by Shuangqing Liu, Juan Li, Yang Fang, Yang Fang, Xiujuan Wu, Yang Cao, Keke Cai, Keke Cai, Jing Yu, Jing Yu, Yan Zhao, Yitao Duan, Yitao Duan

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A Metabolism-Based Interpretable Machine Learning Prediction Model for Diabetic Retinopathy Risk: A Cross-Sectional Study in Chinese Patients with Type 2 Diabetes by Guo-Wei Zong, Wan-Ying Wang, Jun Zheng, Wei Zhang, Wei-Ming Luo, Zhong-Ze Fang, Qiang Zhang

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Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula by Youjeong Youn, Seoyeon Kim, Seung Hee Kim, Yangwon Lee

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Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional th... by Haihong Zhu, Xuan Luo, Zhixian Sun, Yonglin Hua, Yuxin Xiao, Huilong Li, Xiaowu Ma, Wenliang Tan, Zhiqin Xie, Ziyu Zhang, Chenwei Tang, Hongkai Zhuang, Weikai Xu, Yajin Chen, Changzhen Shang

“…The clinical model was built with clinical information. Nine machine learning classifiers were tested and the multilayer perceptron classifier with optimal performance was used as the radiomics model. …”
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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. …”
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A machine learning-based depression risk prediction model for healthy middle-aged and older adult people based on data from the China health and aging tracking study by Fang Xia, Jie Ren, Linlin Liu, Yanyin Cui, Yufang He

“…To develop a scientific basis for depression prevention, machine learning models based on longitudinal data that can assess depression risk are necessary.MethodsData from 2,331 healthy older adults who participated in the China Health and Retirement Longitudinal Study (CHARLS) from 2018 to 2020 were used to develop and validate the predictive model. …”
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Integrating data from unmanned aerial vehicles and Sentinel-2 with PROSAIL-5D-driven machine learning for fuel moisture content estimation in agroecosystems by Jinlong Liu, Jia Jin, Jing Huang, Mengjuan Wu, Shaozheng Hao, Haoyi Jia, Tengda Qin, Yuqing Huang, Dan Chen, Nathsuda Pumijumnong

“…This study presents an advanced framework integrating multi-source remote sensing data fusion, physically based modeling, and machine learning to enable high-resolution and high-precision FMC estimation. …”
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