Development and validation of machine learning models for predicting low muscle mass in patients with obesity and diabetes by Jiaying Ge, Siqi Sun, Jiangping Zeng, Yujie Jing, Huihui Ma, Chunhua Qian, Ran Cui, Shen Qu, Hui Sheng

“…This study aimed to investigate the associations of these indices with LMM and to develop machine learning models for accurate and accessible LMM prediction. …”
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Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete by Mana Alyami, Muhammad Faisal Javed, Irfan Ullah, Hisham Alabduljabbar, Furqan Ahmad

“…This study explores the application of hybrid machine learning models for predicting the compressive strength (CS) of alkali-activated concrete (AAC), a sustainable substitute for traditional Portland cement concrete. …”
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Artificial Intelligence-Driven Models for Predicting Chloride Diffusion in Concrete: A Comparative Regression Analysis by Yongjie Zhang, Yuhan Zhang

“…Utilizing experimental field findings, the application of artificial intelligence (AI) might create models to accurately predict the nonsteady state evident concrete’s chloride diffusion coefficient (Dc) over a prolonged duration. …”
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Performance of Various Artificial Intelligence Models for Predicting Temperature in an Industrial Building—A Case Study by Johan Roussel, Zoubeir Lafhaj, Pascal Yim, Thomas Danel, Laure Ducoulombier

“…This article presents a comparative analysis of the performance of various artificial intelligence models for predicting temperature in an industrial building. …”
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Critical evaluation of feature importance assessment in FFNN-based models for predicting Kamlet-Taft parameters by Yoshiyasu Takefuji

“…Mohan et al. developed a feed-forward neural network (FFNN) model to predict Kamlet-Taft parameters using quantum chemically derived features, achieving notable predictive accuracy. …”
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Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit by Shuxing Wei, Hongmeng Dong, Weidong Yao, Ying Chen, Xiya Wang, Wenqing ji, Yongsheng Zhang, Shubin Guo

“…This study focuses on the development of advanced machine learning (ML) models to accurately predict in-hospital mortality among AP patients admitted to intensive care unit (ICU). …”
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Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel by Christine Ahrends, Mark W Woolrich, Diego Vidaurre

“…One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. …”
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Prognosis of Pediatric Dilated Cardiomyopathy: Nomogram and Risk Score Models for Predicting Death/Heart Transplantation by Bowen Xu, Yue Yuan, Lu Gao, Zhiyuan Wang, Zhenyu Lv, Wen Yu, Hongfang Jin, Zhen Zhen, Zhihui Zhao, Jia Na, Aihua Hu, Yanyan Xiao

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Chemical Composition-Driven Machine Learning Models for Predicting Ionic Conductivity in Lithium-Containing Oxides by Yudai IWAMIZU, Kota SUZUKI, Michiyo KAMIYA, Naoki MATSUI, Kuniharu NOMOTO, Satoshi HORI, Masaaki HIRAYAMA, Ryoji KANNO

“…Thus, new models demonstrating improved prediction ability must be developed. …”
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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. …”
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A comparative study of various statistical and machine learning models for predicting restaurant demand in Bangladesh by Md Shakhawath Hossain, Farjana Parvin

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The application of super-resolution ultrasound radiomics models in predicting the failure of conservative treatment for ectopic pregnancy by Mingyan Zhang, Junfa Sheng

“…This study aimed to develop and validate a predictive model that integrates radiomic features derived from super-resolution (SR) ultrasound images with clinical biomarkers to improve risk stratification. …”
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Comprehensive evaluation of machine learning models for predicting the cognitive status of Alzheimer's disease subjects and susceptible by Lucien Gnegne Meteumba, Vaghawan Prasad Ojha, Shantia Yarahmadian

“…This requires strong predictive models to tease apart what we can do to detect and introduce early-prevention. …”
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Analyzing and predicting global happiness index via integrated multilayer clustering and machine learning models by Boxu Yang, Xiang Xie

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Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors by Deepa Tilwani, Deepa Tilwani, Deepa Tilwani, Deepa Tilwani, Christian O'Reilly, Christian O'Reilly, Christian O'Reilly, Christian O'Reilly, Nicholas Riccardi, Valerie L. Shalin, Valerie L. Shalin, Dirk-Bart den Ouden, Julius Fridriksson, Svetlana V. Shinkareva, Svetlana V. Shinkareva, Amit P. Sheth, Amit P. Sheth, Amit P. Sheth, Rutvik H. Desai, Rutvik H. Desai

“…To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (N = 238) and naming impairment (N = 191) for a cohort of chronic-stage stroke survivors. …”
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General models for predicting the liquid thermal conductivity of fatty acid esters based on smart methods by Chou-Yi Hsu, Ahmad Mohsin, Ramdevsinh Jhala, Nagaraj Patil, Debasish Shit, V.K. Bupesh Raja, Manoj Kumar Ojha, Abinash Mahapatro, Deepak Gupta, Fereydoon Ranjbar

“…A comparison with literature correlations showed that the novel models offer significant improvements in the thermal conductivity prediction. …”
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Prognostic models for predicting patient arrivals in emergency departments: an updated systematic review and research agenda by Luka Petravić, Kaja Gril Rogina, Tit Albreht, Andreja Kukec, Janez Žibert

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Time-Adaptive Machine Learning Models for Predicting the Severity of Heart Failure with Reduced Ejection Fraction by Trevor Winger, Cagri Ozdemir, Shanti L. Narasimhan, Jaideep Srivastava

“…<b>Results:</b> With the progressive introduction of patient-specific data, the model demonstrated significant improvements in predictive capabilities. …”
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Perspective: How complex in vitro models are addressing the challenges of predicting drug-induced liver injury by K. Taylor, R. Ram, R. Ram, L. Ewart, C. Goldring, G. Russomanno, G. P. Aithal, T. Kostrzewski, C. Bauch, J. M. Wilkinson, J. M. Wilkinson, S. Modi, J. G. Kenna, J. G. Kenna, J. Bailey, J. Bailey

“…Predicting which drugs might have the potential to cause drug-induced liver injury (DILI) is highly complex and the current methods, 2D cell-based models and animal tests, are not sensitive enough to prevent some costly failures in clinical trials or to avoid all patient safety concerns for DILI post-market. …”
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Validation of three models (Tolcher, Levine, and Burke) for predicting term cesarean section in Chinese population by Fangcan Sun, Minhong Shen, Bing Han, Youguo Chen, Fangfang Wu

“…Background: Some models predicting cesarean section (CS) have been proposed, with Tolcher, Levine, and Burke model well acknowledged. …”
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