Machine learning-based prediction model for post-stroke cerebral-cardiac syndrome: a risk stratification study by Tingyu Zhang, Zelin Hao, Qunlian Jiang, Linhui Zhu, Lifang Ye

“…This study developed and validated a machine learning (ML) model to predict CCS using clinical, laboratory, and pre-extracted imaging features. …”
Get full text

Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding by Kajornvit Raghareutai, Watcharaporn Tanchotsrinon, Onuma Sattayalertyanyong, Uayporn Kaosombatwattana

“…The proposed risk stratification scores had limited accuracy. We developed a machine learning model to predict the need for endoscopic intervention in patients with acute UGIB. …”
Get full text

Advanced generalized machine learning models for predicting hydrogen–brine interfacial tension in underground hydrogen storage systems by Ahmed Farid Ibrahim

“…Sensitivity analysis and SHAP (Shapley Additive Explanations) analysis revealed temperature as the dominant factor influencing IFT, followed by CO2 concentration and pressure, while divalent salts (CaCl2, MgCl2) exhibited a stronger impact than monovalent salts (NaCl, KCl). This study optimizes hydrogen storage by offering a generalized, high-accuracy ML model that captures nonlinear fluid interactions in H2–brine systems. …”
Get full text

Path Loss Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios by Sarun Duangsuwan, Phakamon Juengkittikul, Myo Myint Maw

“…The purpose of this paper was to predict the path loss characterization of the ground-to-air (G2A) communication channel between the ground sensor (GS) and unmanned aerial vehicle (UAV) using machine learning (ML) models in smart farming (SF) scenarios. …”
Get full text

Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes by Junwei Peng, Xiaoyujie Geng, Yiyue Zhao, Zhijin Hou, Xin Tian, Xinyi Liu, Yuanyuan Xiao, Yang Liu

“…Multiple candidate predictors were screened out by using the importance scores. Four machine learning (ML) algorithms including random forest, extreme gradient boosting, light gradient boosting machine and binary logistic regression were used to construct prediction models. …”
Get full text

Machine learning-based identification of histone deacetylase-associated prognostic factors and prognostic modeling for low-grade glioma by Keshan Wen, Weijie Zhu, Ziyi Luo, Wei Wang

“…Methods Expression data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) were analyzed to identify an optimal HDAC-related risk signature from 73 genes using 10 machine learning algorithms. …”
Get full text

Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn’s disease by Zhuoyan Chen, Weimin Cai, Yuanhang He, Tianhao Mei, Yuxuan Zhang, Shiyu Li, Yiwen Hong, Yuhao Chen, Huiya Ying, Yuan Zeng, Fujun Yu

“…Given the strong association between sarcopenia and IFX treatment outcomes, this study developed computerized tomography radiomics-based machine learning (ML) models, utilizing psoas muscle volume as a proxy for skeletal muscle mass, to predict the response of patients with CD to IFX therapy.Methods In this retrospective study, patients with CD from two institutions were recruited between January 2010 and January 2023, following stringent inclusion and exclusion criteria. …”
Get full text

Machine Learning Model-Based Prediction of In-Hospital Acute Kidney Injury Risk in Acute Aortic Dissection Patients by Zhili Wei, Shidong Liu, Yang Chen, Hongxu Liu, Guangzu Liu, Yuan Hu, Bing Song

“…Machine learning models were built on the training set and validated using the test set. …”
Get full text

Trajectory of breastfeeding among Chinese women and risk prediction models based on machine learning: a cohort study by Yi Liu, Jie Xiang, Ping Yan, Yuanqiong Liu, Peng Chen, Yujia Song, Jianhua Ren

“…Methods This study conducted a three-wave prospective cohort analysis to examine maternal breastfeeding trajectories within the first six months postpartum and to develop risk prediction models for each period using advanced machine learning algorithms. …”
Get full text

Cross-Context Stress Detection: Evaluating Machine Learning Models on Heterogeneous Stress Scenarios Using EEG Signals by Omneya Attallah, Mona Mamdouh, Ahmad Al-Kabbany

“…Although numerous studies have investigated stress detection through machine learning (ML) techniques, there has been limited research on assessing ML models trained in one context and utilized in another. …”
Get full text

Development and validation of machine learning-based diagnostic models using blood transcriptomics for early childhood diabetes prediction by Xin Huang, Xin Huang, Di Ouyang, Weiming Xie, Huawei Zhuang, Siyu Gao, Pan Liu, Lizhong Guo

“…Five feature selection methods (Lasso, Elastic Net, Random Forest, Support Vector Machine, and Gradient Boosting Machine) were employed to optimize gene sets. …”
Get full text

Performance Comparison of 10 State-of-the-Art Machine Learning Algorithms for Outcome Prediction Modeling of Radiation-Induced Toxicity by Ramon M. Salazar, PhD, Saurabh S. Nair, MS, Alexandra O. Leone, MBS, Ting Xu, PhD, Raymond P. Mumme, BS, Jack D. Duryea, BA, Brian De, MD, Kelsey L. Corrigan, MD, Michael K. Rooney, MD, Matthew S. Ning, MD, Prajnan Das, MD, Emma B. Holliday, MD, Zhongxing Liao, MD, Laurence E. Court, PhD, Joshua S. Niedzielski, PhD

“…Purpose: To evaluate the efficacy of prominent machine learning algorithms in predicting normal tissue complication probability using clinical data obtained from 2 distinct disease sites and to create a software tool that facilitates the automatic determination of the optimal algorithm to model any given labeled data set. …”
Get full text

Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

“…Finally, using the cellular automata model, the growth simulation of Tabriz city based on land use and change potential maps obtained from machine learning algorithms for the mentioned periods was performed. …”
Get full text

AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction by Mohamed Sahraoui, Aissa Laouissi, Yacine Karmi, Abderazek Hammoudi, Mostefa Hani, Yazid Chetbani, Ahmed Belaadi, Ibrahim M.H. Alshaikh, Djamel Ghernaout

“…Six predictive models were assessed for accuracy and generalization: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Linear Model (LM), Dragonfly Algorithm-based Deep Neural Network (DNN-DA), and Improved Grey Wolf Optimizer-based Deep Neural Network (DNN-IGWO). …”
Get full text

SVR Data-Driven Optimization of Generator Leading Phase Operation Limit by Dengfeng LI, Mincai YANG, Yuming LIU, Ruilin XU, Xia YU, Zhaojiong LI

“…In view of the difficulty in modeling the mechanism caused by the complex and strong coupling nonlinearities between the multiple variables in the limiting conditions of leading phase operation, a novel method is proposed in this paper to optimize the leading phase operation limit of generator based on data-driven support vector machine regression (SVR). …”
Get full text

Enhanced Light-Gradient Boosting Machine (GBM)-Based Artificial Intelligence-Blockchain-Based Telesurgery in Sixth Generation Communication Using Optimization Concept by Punitha S., Preetha K. S.

Get full text

Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets by Amin Salemnia, Seyedehmaryam Hosseini Boldaji, Vida Atashi, Manoochehr Fathi-Moghadam

“…This study emphasizes the importance of the Froude number in predicting jet behavior and shows the efficacy of advanced machine learning models in capturing complex fluid dynamics, providing valuable insights for optimizing engineering applications such as water jet cutting and cooling systems.…”
Get full text

Machine learning applications in the analysis of sedentary behavior and associated health risks by Ayat S Hammad, Ayat S Hammad, Ali Tajammul, Ismail Dergaa, Ismail Dergaa, Ismail Dergaa, Maha Al-Asmakh, Maha Al-Asmakh

“…As prolonged inactivity becomes a growing public health concern, researchers are increasingly utilizing machine learning (ML) techniques to examine and understand these patterns. …”
Get full text

Optimization of multi-element geochemical anomaly recognition in the Takht-e Soleyman area of northwestern Iran using swarm-intelligence support vector machine by Hamid Sabbaghi, Seyed Hassan Tabatabaei, Nader Fathianpour

“…Therefore, detecting metal resources under barren cover is a significant step for industrial progress. The application of optimized machine learning algorithms is critical for detecting undiscovered deposits under barren cover. …”
Get full text

Machine learning-driven optimization of culture conditions and media components to mitigate charge heterogeneity in monoclonal antibody production: current advances and future pers... by Hossein Kavoni, Iman Shahidi Pour Savizi, Saratram Gopalakrishnan, Nathan E. Lewis, Seyed Abbas Shojaosadati

“…This review highlights machine learning (ML) as a powerful approach for modeling these relationships and forecasting charge variant profiles in CHO cell-based mAb process development. …”
Get full text