Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree regression by Idris Zubairu Sadiq, Fatima Sadiq Abubakar, Muhammad Auwal Saliu, Babangida Sanusi katsayal, Aliyu Salihu, Aliyu Muhammad

Subjects: “…Machine learning…”
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Forecasting Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms by Rafael Pires de Lima, Yue Chen, Youzuo Lin

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Automated post-run analysis of arrayed quantitative PCR amplification curves using machine learning [version 1; peer review: awaiting peer review] by David Garrett Brown, Darwin J. Operario, Lan Wang, Shanrui Wu, Daniel T. Leung, Eric R. Houpt, James A. Platts-Mills, Jie Liu, Ben J. Brintz

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A Novel Modeling Technique for the Forecasting of Multiple-Asset Trading Volumes: Innovative Initial-Value-Problem Differential Equation Algorithms for Reinforcement Machine Learning by Mazin A. M. Al Janabi

“…In addition, they can be applied to artificial intelligence and machine learning for the policymaking process, reinforcement machine learning techniques for the Internet of Things (IoT) data analytics, expert systems in finance, FinTech, and within big data ecosystems.…”
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A hybrid approach to analyzing factors influencing international student retention in Turkish higher education: integrating fuzzy DEMATEL, machine learning, and statistical methods by Serra Usta Ergun

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Study on the Forecasting of Internal Solitary Wave Propagation in the Andaman Sea Using Joint Ascending-Descending Orbit Sentinel-1A Data and Machine Learning by Zexiang Cao, Junmin Meng, Jing Wang, Lina Sun, Hao Zhang

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Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach by Seyyed Hossein Mousavie Anijdan, Daryush Moslemi, Reza Reiazi, Hamid Fallah Tafti, Ali Akbar Moghadamnia, Reza Paydar

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Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. by Tiago de Oliveira Barreto, Fernando Lucas de Oliveira Farias, Nicolas Vinícius Rodrigues Veras, Pablo Holanda Cardoso, Gleyson José Pinheiro Caldeira Silva, Chander de Oliveira Pinheiro, Maria Valéria Bezerra Medina, Felipe Ricardo Dos Santos Fernandes, Ingridy Marina Pierre Barbalho, Lyane Ramalho Cortez, João Paulo Queiroz Dos Santos, Antonio Higor Freire de Morais, Gustavo Fontoura de Souza, Guilherme Medeiros Machado, Márcia Jacyntha Nunes Rodrigues Lucena, Ricardo Alexsandro de Medeiros Valentim

“…This study explored different machine learning models in the RegulaRN database, from October 2021 to January 2024, totaling 47,056 regulations. …”
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A novel machine learning-based immune prognostic signature for improving clinical outcomes and guiding therapy in colorectal cancer: an integrated bioinformatics and experimental study by Yuanchun Zhao, Dexu Xun, Jiajia Chen, Xin Qi

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Letter to the editor regarding ‘Correlation between neutrophil-to-lymphocyte ratio and contrast-induced acute kidney injury and the establishment of machine-learning-based predictive models’ by Ning Zhuo, Gang Wang, Gang Wu

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Correcting forest aboveground biomass biases by incorporating independent canopy height retrieval with conventional machine learning models using GEDI and ICESat-2 data by Biao Zhang, Zhichao Wang, Tiantian Ma, Zhihao Wang, Hao Li, Wenxu Ji, Mingyang He, Ao Jiao, Zhongke Feng

“…Spaceborne LiDAR satellites, including GEDI and ICESat-2, have shown significant potential in estimating aboveground biomass (AGB) using machine learning (ML) methods. In contrast to advances focused on the refinement of ML algorithms, this study aims to enhance AGB estimation accuracy by integrating an additional Canopy Height (CH) information. …”
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Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area by Jiaer Yang, Jiaer Yang, Pinglang Kou, Pinglang Kou, Xu Dong, Xu Dong, Ying Xia, Qinchuan Gu, Yuxiang Tao, Jiangfan Feng, Qin Ji, Weizao Wang, Ram Avtar

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Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the “RegulaRN Leitos Gerais” platform by Tiago de Oliveira Barreto, Fernando Lucas de Oliveira Farias, Nicolas Vinícius Rodrigues Veras, Pablo Holanda Cardoso, Gleyson José Pinheiro Caldeira Silva, Chander de Oliveira Pinheiro, Maria Valéria Bezerra Medina, Felipe Ricardo dos Santos Fernandes, Ingridy Marina Pierre Barbalho, Lyane Ramalho Cortez, João Paulo Queiroz dos Santos, Antonio Higor Freire de Morais, Gustavo Fontoura de Souza, Guilherme Medeiros Machado, Márcia Jacyntha Nunes Rodrigues Lucena, Ricardo Alexsandro de Medeiros Valentim

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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

“…By integrating the MIMIC-IV database and machine learning algorithms, we developed an effective predictive model for HRS in liver cirrhosis patients, providing a robust tool for early clinical intervention.…”
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Estado de Bienestar Digital en Ecuador: Datificación Ciudadana y Machine Learning en la construcción y gestión de la pobreza by Alexandra Gualavisí

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Predicting the risk of gastroparesis in critically ill patients after CME using an interpretable machine learning algorithm – a 10-year multicenter retrospective study by Yuan Liu, Songyun Zhao, Wenyi Du, Wei Shen, Ning Zhou

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A Nonintrusive Nonlinear Model Reduction Method for Thermal Cycling-Induced Viscoplastic Deformation Problems Based on Segmented Gaussian Process Regression Machine Learning by Jiunn-Horng Lee, Chia-Hsiu Ou, Chih-Min Yao, Ming-Hsiao Lee

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Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation by Ruixin Wu, Ruixin Wu, Sihao Chen, Sihao Chen, Yi He, Yi He, Ya Li, Song Mu, Aishun Jin, Aishun Jin

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Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review by M. E. Alqaysi, A. S. Albahri, Rula A. Hamid

“…It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. …”
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Machine Learning-Devised Immune-Related lncRNA Signature Panel Predicts the Prognosis and Immune Landscape in Breast Cancer Novel IRLP Signature in BRCA by Jun-Yu Zhu, An-Qi Lyu, Zhang-Ting Wang, Wai-Yee Chan, Tao Qin, Kai-Kei Miu, He-Rui Yao

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