Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete by Miljan Kovačević, Marijana Hadzima-Nyarko, Predrag Petronijević, Tatijana Vasiljević, Miroslav Radomirović

“…The evaluation was based on their predictive accuracy. The optimal model identified was the GPR ARD Exponential model, which achieved a mean absolute error (MAE) of 1.8953 MPa and a correlation coefficient (R) of 0.9658. …”
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Unveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese women by Xiao Fei Nie, Lan Lan Xu, Wen Ping Guo, Jin Hui Li, Li Cheng, Tao Tao Zhang, Jun-Yan Li

“…This study aims to explore the factors associated with postpartum posttraumatic stress disorder (PP-PTSD) in Chinese women using decision tree and logistic regression models, while also comparing the predictive performance of both approaches. …”
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Comparative Analysis of Artificial Neural Networks with Classical Regression Models for Predicting Dissolved Oxygen in Water by Ana Ivette Jater Ruiz, Francisco Primero Primero, Roberto Alejo Eleuterio, Francisco Javier Illescas Martínez, Federico Del Razo López, Everardo Granda

“…In this study, we evaluate the effectiveness of Artificial Neural Networks (ANNs) in predicting DO levels by comparing seven different ANN architectures to nine classical regression models. …”
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Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review by Morris Ogero, Samuel Akech, Mike English, Jalemba Aluvaala, Ambrose Agweyu, Lucas Malla, Rachel Jelagat Sarguta, Nelson Owuor Onyango

“…Objectives To identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).Design Systematic review of peer-reviewed journals.Data sources MEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.Eligibility criteria We included model development studies predicting in-hospital paediatric mortality in LMIC.Data extraction and synthesis This systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. …”
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Comparative analysis of machine learning models for predicting water quality index in Dhaka’s rivers of Bangladesh by Mosaraf Hosan Nishat, Md. Habibur Rahman Bejoy Khan, Tahmeed Ahmed, Syed Nahin Hossain, Amimul Ahsan, M. M. El-Sergany, Md. Shafiquzzaman, Monzur Alam Imteaz, Mohammad T. Alresheedi

“…To our knowledge, this is the first study to apply such a comprehensive range of ML models to predict the WQI of Dhaka’s four major rivers. …”
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A Comparative Analysis of the Effectiveness of Multiple Models for Predicting Heart Failure using Data Mining by Ahmed Sami Jaddoa, Juliet Kadum, Amaal Kadum

“…For forecasting, decision-making, and disease prediction, DM technologies are essential. This research predicts heart disease using DM algorithms. …”
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Interpretable machine learning models for predicting childhood myopia from school-based screening data by Qi Feng, Xin Wu, Qianwen Liu, Yuanyuan Xiao, Xixing Zhang, Yan Chen

“…Abstract This study assessed the efficacy of various diagnostic indicators and machine learning (ML) models in predicting childhood myopia. A total of 2,365 children aged 5–12 years were included in the study. …”
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MATHEMATICAL MODELS PREDICTING LEUKOPENIA AND NEUTROPENIA IN PATIENTS WITH CHRONIC HEPATITIS C IN THE BACKGROUND INTERFERONCONTAINING SCHEMES by I. G. Bakulin, N. Kh. Dianova, Yu. G. Sandler, M. Yu. Prostov

“…Prognostic criteria were identified, indicating the possible development  of the LP and NP expressed during treatment with interferon: female  gender,  low initial load, TT-genotype of IL-28B, the  initial level of white  blood cells and neutrophils  below 5,7×109/L and 3,4×109/L, respectively. Mathematical  models predicting the onset of LP and NP, formalized in the form of decision trees were also constructed. …”
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Development of Advanced Machine Learning Models for Predicting CO₂ Solubility in Brine by Xuejia Du, Ganesh C. Thakur

“…This study explores the application of advanced machine learning (ML) models to predict CO₂ solubility in NaCl brine, a critical parameter for effective carbon capture, utilization, and storage (CCUS). …”
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Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. by Caroline Colijn, Aaron Brandes, Jeremy Zucker, Desmond S Lun, Brian Weiner, Maha R Farhat, Tan-Yun Cheng, D Branch Moody, Megan Murray, James E Galagan

“…In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. …”
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Predicting Wind Turbine Blade Tip Deformation With Long Short‐Term Memory (LSTM) Models by Shubham Baisthakur, Breiffni Fitzgerald

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Predicting Crude Oil Prices During a Pandemic: A Comparison of Arima and Garch Models by Mohammad Imdadul Haque, Abdul Rahman Shaik

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Leveraging deep neural network and language models for predicting long-term hospitalization risk in schizophrenia by Yihang Bao, Wanying Wang, Zhe Liu, Weidi Wang, Xue Zhao, Shunying Yu, Guan Ning Lin

“…By utilizing multimodal features, our deep learning model achieved a classification accuracy of 0.81 and an AUC of 0.9. …”
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Predicting police and military violence: evidence from Colombia and Mexico using machine learning models by Juan David Gelvez

“…This article proposes the use of machine learning models to predict armed forces violence at the municipality level. …”
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Learning models for predicting pavement friction based on non-contact texture measurements: Comparative assessment by Xiuquan Lin, You Zhan, Zilong Nie, Joshua Qiang Li, Xinyu Zhu, Allen A. Zhang

“…By assessing the importance of the 38 parameter variables, the most critical 21 variables were selected for model development. Test results demonstrate that the GBDT model exhibits the best predictive performance, with an explanatory capability of 87.4​% for road friction performance. …”
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Technology for Improving the Accuracy of Predicting the Position and Speed of Human Movement Based on Machine Learning Models by Artem Obukhov, Denis Dedov, Andrey Volkov, Maksim Rybachok

“…For speed prediction, the linear regression (LR) model showed the best results when the analysed window length was 10 frames. …”
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Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength by Hayder Riyadh Mohammed Mohammed, Sumarni Ismail

“…Nine input combinations were constructed based on the statistical correlation to be supplied for the proposed predictive model. The prediction accuracy of the RF model was validated against the Support Vector Machine (SVM), and several other empirical formulations have been adopted in the literature. …”
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Predicting the Energy Consumption in Chillers: A Comparative Study of Supervised Machine Learning Regression Models by Mohamed Salah Benkhalfallah, Sofia Kouah, Saad Harous

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Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models by Ziyang Zhang, Jiancheng Ye

“…The SHAP analysis revealed that these predictors had a substantial influence on model predictions, underscoring their importance in assessing mortality risk in this patient population.ConclusionDeep learning models, particularly the 1D CNN, demonstrated superior predictive accuracy compared to traditional ML models in predicting mortality among critically ill patients with hypertension. …”
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Hybrid Machine Learning Models for Predicting the Impact of Light Wavelengths on Algal Growth in Freshwater Ecosystems by Himaranga Sumanasekara, Harshi Jayasingha, Gayan Amarasooriya, Narada Dayarathne, Bandita Mainali, Lalantha Senevirathna, Ashoka Gamage, Othmane Merah

“…The integration of empirical data with machine learning offers a robust framework for predictive modeling in algal research and industrial applications.…”
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