Enhanced prediction of corrosion rates of pipeline steels using simulated annealing-optimized ANFIS models by Ali Hussein Khalaf, Bing Lin, Ahmed N. Abdalla, Zhongzhi Han, Ying Xiao, Junlei Tang

“…The SA-ANFIS model offers a robust, optimized tool for predicting corrosion in petroleum pipelines, significantly improving prediction accuracy under harsh conditions.…”
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Comparative Analysis of Regression Models for Stock Price Prediction: Linear, Support Vector, Polynomial, and Lasso by Ștefan Rusu, Marcel Ioan Boloș, Marius Leordeanu

“…Overall, the study highlights the predictive power of simpler regression models over more complex ones in stock price predictions and offers recommendations for model selection.…”
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Predictions of Spartina alterniflora leaf functional traits based on hyperspectral data and machine learning models by Wei Li, Xueyan Zuo, Zhijun Liu, Leichao Nie, Huazhe Li, Junjie Wang, Zhiguo Dou, Yang Cai, Xiajie Zhai, Lijuan Cui

“…Using original spectral and first-order differential conversion data of feature bands, we established four prediction models: random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and back propagation neural network (BPNN). …”
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Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi‐kernel genomic prediction models integrating secondary traits and environmental covariates by Sudip Kunwar, Md Ali Babar, Diego Jarquin, Yiannis Ampatzidis, Naeem Khan, Janam Prabhat Acharya, Jordan McBreen, Samuel Adewale, Gina Brown‐Guedira

“…This study developed genomic prediction models to estimate these traits using diverse statistical methods while enhancing predictive ability (PA) by integrating environmental covariates (ECs) and secondary traits. …”
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Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study by Gaoyang Li, Shrikant S. Kolan, Franco Grimolizzi, Joseph Sexton, Giulia Malachin, Guro Goll, Tore K. Kvien, Tore K. Kvien, Nina Paulshus Sundlisæter, Manuela Zucknick, Siri Lillegraven, Espen A. Haavardsholm, Espen A. Haavardsholm, Bjørn Steen Skålhegg

“…The predictive power of each feature was assessed using a composite measure derived from individual algorithm estimates.ResultsThe model demonstrated a mean AUC-ROC of 0.75-0.76, with mean sensitivity of 0.77-0.81, precision (also referred to as Positive Predictive Value) of 0.77-0.79 and specificity of 0.63-0.66 across the criteria. …”
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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective Study by Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

“…ConclusionsThis study suggests that incorporating breast sonography into models with clinical and laboratory data may modestly improve pCR prediction. …”
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Establishment and validation of risk prediction model to predict intravenous immunoglobulin-resistance in Kawasaki disease based on meta-analysis of 15 cohorts by Shuhui Wang, Na Sun, PanPan Liu, Weiguo Qian, Qiuqin Xu, DaoPing Yang, Mingyang Zhang, Miao Hou, Ye Chen, Guanghui Qian, Chunmei Gao, Ling Sun, Haitao Lv

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Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007–2018 by Efrain Riveros Perez, Bibiana Avella-Molano

“…Objectives This study aimed to compare the performance of five machine learning algorithms to predict diabetes mellitus based on lifestyle factors (diet and physical activity).Design Retrospective cross-sectional predictive modelling study.Setting This study was conducted using publicly available data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative survey designed to assess the health and nutritional status of the US population.Participants We analysed data from 29 509 non-pregnant adults who participated in NHANES between 2007 and 2018.Primary and secondary outcome measures The primary outcome was the prediction of type 2 diabetes mellitus (T2DM) by self-reported responses based on machine learning models. …”
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Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance by Leonel Patrício, Leonilde Varela, Zilda Silveira

“…This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. …”
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Comparative Analysis of Classification Algorithms for Predicting Membership Churn in Fitness Centers: Case Study and Predictive Modeling at EightGym Indonesia by Dewi Lestari Mu'ti, Putri Taqwa Prasetyaningrum

“…The methodology follows the CRISP-DM framework, covering business understanding, data preparation, modeling, evaluation, and deployment stages. Evaluation results indicate that XGBoost delivers the best performance with 95% accuracy, high recall, and F1-score, making it the most effective algorithm for churn prediction in this context. …”
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Cyclic dual latent discovery for improved blood glucose prediction through patient–provider interaction modeling: a prediction study by Suyeon Park, Seoyoung Kim, Dohyoung Rim

“…Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. …”
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Developing the risk prediction model (ProlncSig) from lipoxygenase pathway-related lncRNAs for prognosis prediction in breast cancer by Xiaoyu Fu, Weixing Wang, Bradley M. Downs, Juanjuan Li

“…Information from our analysis was used to construct a risk prediction model (ProlncSig) to predict breast cancer prognosis. …”
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Machine Learning-Based Prediction Model for Predicting the Effect of the Serum γKlotho Level on Susceptibility to Coronary Heart Disease by Guo ZT, Yu XL, Cheng H, Naman T

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Development and indirect validation of a model predicting frailty in the French healthcare claims database by Hana Lahbib, Laurence Mandereau-Bruno, Sarah Goria, Vérène Wagner, Marion J Torres, Catherine Féart, Catherine Helmer, Karine Pérès, Laure Carcaillon-Bentata

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Clinical and immunological predictors of severe pertussis in children: a nomogram-based prediction model by Shiying Zhang, Na Shan, Junfang Qin, Ying Li, Chang Liu, Yuejie Yang

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Development and validation of a risk prediction model for unplanned 7-day readmission to PICU by Min Ding, Chunfeng Yang, Yumei Li

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Developing a risk prediction model for sudden cardiac death in children with hypertrophic cardiomyopathy by Zhen Zhen, Yeqiong Xu, Xi Chen, Jia Na, Yanyan Xiao, Yue Yuan

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Development of prediction model for Clostridium sporogenes spores growth under different experimental conditions by DONG Qing-li, GUO Li-yang, TU Kang, LI Hong-wen

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Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, V. V. Dzhakhnaev

“…The abstract describes the construction of a binary classification model for predicting the type of job advertisement in cloud-based ATS (Applicant Tracking Systems) as either legitimate or fraudulent. …”
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Prediction of bloodstream infection using machine learning based primarily on biochemical data by Ramtin Zargari Marandi, Frederik Boetius Hertz, Jesper Qvist Thomassen, Steen Christian Rasmussen, Ruth Frikke-Schmidt, Niels Frimodt-Møller, Karen Leth Nielsen, Cameron Ross MacPherson

“…SHapley Additive exPlanations (SHAP) identified platelets, leukocytes, and neutrophils-to-lymphocytes as the top-3 predictive features. The model showed higher sensitivity (average 0.66) for common pathogens, e.g., 0.71 for E. coli. …”
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