Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients by Liping Xu, Fang Cao, Lian Wang, Weihua Liu, Meizhu Gao, Li Zhang, Fuyuan Hong, Miao Lin

“…The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. …”
Get full text

A study on the risk prediction model for venous thromboembolism in orthopedic inpatients based on machine learning by Bo Zhang, Yumei Qin, Liandi Jiu, Chunming Qin, Jiangbo Wang, Haiqing Zhao

“…ObjectiveTo construct a venous thromboembolism (VTE) risk prediction model for orthopedic inpatients using machine learning modeling techniques, identify high-risk patients, and optimize clinical interventions.MethodsThis study involved a retrospective analysis of 286 orthopedic inpatients from Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region) from January 1, 2022 to December 31, 2022. …”
Get full text

Photovoltaic Farm Power Generation Forecast Using Photovoltaic Battery Model with Machine Learning Capabilities by Agboola Benjamin Alao, Olatunji Matthew Adeyanju, Manohar Chamana, Stephen Bayne, Argenis Bilbao

“…This study presents a machine learning-based photovoltaic (PV) model for energy management and planning in a microgrid with a battery system. …”
Get full text

Multi-Objective Automated Machine Learning for Inversion of Mesoscopic Parameters in Discrete Element Contact Models by Xu Ao, Shengpeng Hao, Yuyu Zhang, Wenyu Xu

“…Although conventional machine learning techniques have been applied to invert the contact model parameters, they are hampered by the difficulty of selecting the optimal hyperparameters and, in some cases, insufficient data, which limits both the predictive accuracy and robustness. …”
Get full text

Predicting Olympic Medal Performance for 2028: Machine Learning Models and the Impact of Host and Coaching Effects by Zhenkai Zhang, Tengfei Ma, Yunpeng Yao, Ningjia Xu, Yujie Gao, Wanwan Xia

“…This study develops two machine learning models to predict the medal performance of countries at the 2028 Olympic Games while systematically analyzing and quantifying the impacts of the host effect and exceptional coaching on medal gains. …”
Get full text

Explainable machine learning model for predicting the transarterial chemoembolization response and subtypes of hepatocellular carcinoma patients by Yunjie Zhang, Songjian Tong, Junhui Yang, Jiawei Lin, Yifan Kong, Deyu Lu, Yan Chen, Yingchao Li, Linfeng Xu, Xiuyan Kong, Guoqing Zhu, Hao Zhang, Pixu Liu, Zhijie Yu, Jinglin Xia

“…Abstract Background Hepatocellular carcinoma (HCC), the third leading cause of cancer-related deaths globally, faces heterogeneous responses to transarterial chemoembolization (TACE) in intermediate-stage disease. We developed a Machine Learning (ML)-based model integrating routine clinical variables to preoperatively predict TACE efficacy, enabling tailored TACE candidate selection and optimized therapeutic decision-making. …”
Get full text

Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children by Jintuo Zhou, Yongjin Xie, Ying Liu, Peiguang Niu, Huajiao Chen, Xiaoping Zeng, Jinhua Zhang

“…Interpretation of the optimal model was conducted using shapley additive explanations (SHAP). …”
Get full text

Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

“…Visualize the constructed optimal machine learning model using the SHapley additive interpretation (SHAP) value method. …”
Get full text

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. …”
Get full text

Research on prediction model of adolescent suicide and self-injury behavior based on machine learning algorithm by Yao Gan, Li Kuang, Xiao-Ming Xu, Ming Ai, Jing-Lan He, Wo Wang, Su Hong, Jian mei Chen, Jun Cao, Qi Zhang

“…Six methods—multi-level perceptron, random forest, K-nearest neighbor, support vector machine, logistic regression, and extreme gradient boosting—were used to build predictive models. …”
Get full text

Second-generation downscaled earth system model data using generative machine learningOEDI by Grant Buster, Brandon N. Benton, Deeksha Rastogi, Shih-Chieh Kao, Guilherme Castelao, Jordan Eisenman

“…All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias.The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. …”
Get full text

A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Subsequently, based on 482 prognostic moDEGs, we developed and validated an optimal model, termed the Monocyte-related Gene Prognostic Signature (MGPS), by integrating 101 predictive models generated from 10 machine learning algorithms across multiple transcriptome sequencing datasets. …”
Get full text

Developing an Interpretable Machine Learning Model for Early Prediction of Cardiovascular Involvement in Systemic Lupus Erythematosus by Deng Z, Liu H, Chen F, Liu Q, Wang X, Wang C, Lyu C, Li J, Li T

“…Notably, the GBM model showed optimal performance, and its interpretability allowed clinicians to visualize decision-making processes, facilitating early identification of high-risk patients.Keywords: systemic lupus erythematosus, cardiovascular involvement, machine learning, prediction model, interpretability…”
Get full text

Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face recognition by Sonal, Ajit Singh, Chander Kant

“…The integration of support vector machine (SVM) and random forest (RF) classifiers, along with optimization techniques like bacterial foraging optimization (BFO) and genetic algorithms (GA), improves efficiency and robustness. …”
Get full text

Thermodynamic calculation-assisted design of 500 MPa high performance steel by machine learning by Weiyi Gong, Jinshan He, Fan Wang, Xitao Wang

“…In order to rationally design Q500 low-alloy wind power steel with stable microstructure and properties within a large cooling rate range, this study proposed a design system that utilizes thermodynamic database, machine learning (ML), and the multi-objective genetic optimization algorithm (MOGA). …”
Get full text

Research on Optimization of Magnetic Ranging by Mechanism & Intelligence by QIAN Hongyu, CHE Yang, WANG Xiufang, FENG Fuping, JIANG Chunlei

Get full text

Interpretable machine learning approach to predict Hepatitis C virus NS5B inhibitor activity using voting-based LightGBM and SHAP by Teuku Rizky Noviandy, Aga Maulana, Irvanizam Irvanizam, Ghazi Mauer Idroes, Nur Balqis Maulydia, Trina Ekawati Tallei, Muhammad Subianto, Rinaldi Idroes

Get full text

Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…ML-driven solutions, including LSTM networks and random forest models, enable real-time anomaly detection (e.g., 85% accurate algal bloom prediction 7 days in advance) and operational optimization (15% cost reduction in wastewater treatment). …”
Get full text

Triple-E Principle: Leveraging Occam’s Razor for Dance Energy Expenditure Estimation by Kuan Tao, Kun Meng, Bingcan Gao, Junchao Yang, Junqiang Qiu

Subjects: Get full text

Optimal shapelets tree for time series interpretable classification by Lorenzo Bonasera, Stefano Gualandi

Get full text