Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach by Fetlework Gubena Arage, Zinabu Bekele Tadese, Eliyas Addisu Taye, Tigist Kifle Tsegaw, Tsegasilassie Gebremariam Abate, Eyob Akalewold Alemu

“…Conclusion This study demonstrates that the ensemble machine learning models, such as Extra Trees Classifier and Random Forest, are promising in predicting cervical cancer screening uptake among African women with accuracies of 94.13% and 93.87%, respectively. …”
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Decoding healthcare resilience for sustainable development goal 3: A machine learning analysis of global health systems by Ibrahim Alnafrah, Alexander Poroshin

“…The framework combines unsupervised learning — Principal Component Analysis (PCA) for dimensionality reduction and structural insight, and K-means for risk-level clustering — with supervised classification models, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF), Classification and Regression Trees (CART), and Linear Discriminant Analysis (LDA), for predictive analysis. …”
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An explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of Portugal by Silvia García-Méndez, Francisco de Arriba-Pérez, Fátima Leal, Bruno Veloso, Benedita Malheiro, Juan Carlos Burguillo-Rial

“…The proposed method implements a processing pipeline comprised of sample pre-processing, incremental classification with Machine Learning models, and outcome explanation. …”
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Data driven tensile strength prediction for fiber-reinforced rubberized recycled aggregate concrete using machine learning by Avijit Pal, Khondaker Sakil Ahmed, Nur Yazdani

“…To tackle this, this research examined the tensile strength behavior of fiber-reinforced rubberized recycled aggregate concrete (FR3C) using nine machine learning (ML) models. In this study, nine machine learning models—Random Forest, K-Nearest Neighbors, Support Vector Regression, Decision Tree, Artificial Neural Network, AdaBoost, Gradient Boost, CatBoost, and Extreme Gradient Boost—were trained and tested using a dataset of 346 samples representing various mix proportions. …”
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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

“…This study aims to evaluate the compressive strength (CS) of high strength glass-powder concrete (HSGPC) using machine learning (ML) models and enhance predictive accuracy through hybrid optimization techniques. …”
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Predicting patient outcomes and risk for revision surgery after hip and knee replacement surgery: study protocol for a comparison of modelling approaches using the Swiss National J... by Léonie Hofstetter, Nathalie Schweyckart, Christof Seiler, Christian Brand, Laura C. Rosella, Mazda Farshad, Milo A. Puhan, Cesar A. Hincapié

“…Abstract Background Prediction of postoperative patient-reported outcomes and risk for revision surgery after total hip arthroplasty (THA) or total knee arthroplasty (TKA) can inform clinical decision-making, health resource allocation, and care planning. Machine learning (ML) algorithms are increasingly used as an alternative to traditional logistic regression (LR) prediction, but there is uncertainty about their superiority in overall model performance. …”
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Towards Improved Turbomachinery Measurements: A Comprehensive Analysis of Gaussian Process Modeling for a Data-Driven Bayesian Hybrid Measurement Technique by Gonçalo G. Cruz, Xavier Ottavy, Fabrizio Fontaneto

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Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China by Zeqiang Chen, Lei Wu, Nengcheng Chen, Ke Wan

“…Good results were obtained on nine eddy covariance sites in China. The model was also compared with the random forest, long short-term memory, deep neural network, and convolutional neural networks (1D) models to distinguish it from previous shallow machine learning models to estimate NEE, and the results show that deep learning models have great potential in NEE modeling. …”
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Optimizing ML models for cybercrime detection: balancing performance, energy consumption, and carbon footprint through multi-objective optimization by Romil Rawat

“…Abstract This study aims to enhance computational performance while minimizing environmental impact in AI (Artificial Intelligence) and ML (Machine Learning) applications, especially in cybersecurity, by developing energy-efficient models using a multi-objective optimization approach. …”
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Learning Propagators for Sea Surface Height Forecasts Using Koopman Autoencoders by Andrew E. Brettin, Laure Zanna, Elizabeth A. Barnes

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Optimizing Space Heating in Buildings: A Deep Learning Approach for Energy Efficiency by Fernando Almeida, Mauro Castelli, Nadine Corte-Real, Luca Manzoni

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Prediction of Shield Tunneling Attitude Based on WM-CTA Method by GAO Su, CHEN Cheng

“…To ensure that shield tunneling closely aligns with the designed alignment and to improve engineering construction quality, this study proposes a novel shield attitude prediction model, called WM-CTA, based on deep learning technology. …”
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Advanced genetic algorithm (GA)-independent component analysis (ICA) ensemble model for predicting trapped humans through hybrid dimensionality reduction by Enoch Adama Jiya, Ilesanmi B. Oluwafemi

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Facial Emotion Recognition of 16 Distinct Emotions From Smartphone Videos: Comparative Study of Machine Learning and Human Performance by Marie Keinert, Simon Pistrosch, Adria Mallol-Ragolta, Björn W Schuller, Matthias Berking

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Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data by Patrick Long, Andres Quintero, Javier Lopez-Molina, Merina Su, Nicola Boulter, Cindy Weber, Ralica Dimitrova

“…Objectives To identify and characterise distinct subgroups of patients with asthma with severe acute exacerbations (AEs) by using a multistep clustering methodology that combines supervised and unsupervised machine learning.Methods This cohort study used anonymised, all-payer medical and prescription US claim data from October 2015 to May 2022. …”
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EUS-based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer by Shuangyang Mo, Shuangyang Mo, Nan Yi, Fengyan Qin, Huaying Zhao, Huaying Zhao, Yingwei Wang, Haiyan Qin, Haixiao Wei, Haixing Jiang, Shanyu Qin

“…Six machine learning algorithms were utilized to train prediction models employing features with nonzero coefficients. …”
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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…This study proposes a machine learning (ML) system that integrates regression-based eGFR estimation, metaheuristic optimization using the Grey Wolf Optimizer (GWO), and multi-class classification with various ML models to enhance CKD staging and classification. …”
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A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure by Shubhendu Vikram Singh, Sufyan Ghani

“…This study establishes a threshold criterion based on the ratio of the penetration rate to the dynamic resistance (e/qd), where values exceeding four indicate high liquefaction susceptibility. ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. …”
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Comparing the effect of pre-anesthesia clonidine and tranexamic acid on intraoperative bleeding volume in rhinoplasty: a machine learning approach by Zahra Asghari Varzaneh, Akram Hemmatipour, Hadi Kazemi-Arpanahi

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Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease. by Laura-Jayne Gardiner, Anna Paola Carrieri, Karen Bingham, Graeme Macluskie, David Bunton, Marian McNeil, Edward O Pyzer-Knapp

“…We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to integrate multi-modal data and predict inter-patient variation in drug response. …”
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