Prediction of ischemic stroke in patients with H-type hypertension based on biomarker by Ke Chen, Jianxun He, Lan Fu, Xiaohua Song, Ning Cao, Hui Yuan

“…Our study aimed to develop and validate a biomarker-based prediction model for ischemic stroke in Hyperhomocysteinemia-type (H-type) hypertension patients. …”
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Robust Model Predictive Control for AFE-Inverter Drives With Common Mode Voltage Elimination by S.M. Muslem Uddin, Galina Mirzaeva, Graham C. Goodwin

“…This paper proposes a novel and robust version of Model Predictive Control scheme for AC drives based on Voltage Source Inverter (VSI) with Active Front End (AFE). …”
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Tube-Based Robust Model Predictive Control for Autonomous Vehicle with Complex Road Scenarios by Yang Chen, Youping Sun, Junming Li, Jiangmei He, Chengwei He

“…This study proposes a Tube-based Robust Model Predictive Control (Tube-RMPC) strategy for autonomous vehicle control to address model parameter uncertainties and variations in road–tire adhesion coefficients in complex road scenarios. …”
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Simplified Model Predictive for Controlling Circulating and Output Currents of a Modular Multilevel Converter by Abolfazl Sheybanifar, Seyed Masoud Barakati

“…Model Predictive Control (MPC) has attracted wide attention recently, especially in electrical power converters. …”
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Nonlinear Model Predictive Control for Trajectory Tracking of Omnidirectional Robot Using Resilient Propagation by Mahmoud El-Sayyah, Mohamad R. Saad, Maarouf Saad

“…This paper proposes an enhanced Nonlinear Model Predictive Control (NMPC) framework that incorporates a robust, convergent variant of the resilient propagation (RPROP) algorithm to efficiently solve the Nonlinear Optimization Problem (NOP) in real time. …”
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Bayesian Architecture for Predictive Monitoring of Unbalance Faults in a Turbine Rotor–Bearing System by Banalata Bera, Shyh-Chin Huang, Po Ting Lin, Yu-Jen Chiu, Jin-Wei Liang

“…This validates the Bayesian framework for predictive monitoring and maintenance-oriented solutions.…”
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A predictive model of cognitive impairment in Parkinson's disease based on multivariate logistic regression by BA Mengru, YIN Xiaohong, LI Shaoyuan

“…Parkinson's disease (PD) patients were often accompanied by cognitive impairment, which seriously affected the quality of life, so the over-prediction of cognitive impairment in Parkinson's disease was crucial for clinical diagnosis and intervention. …”
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Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

“…The network model achieves a prediction accuracy of 92.5 % for the classification of structural fire-related casualty severities. …”
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Data-driven predictive model of coal permeability based on microscopic fracture structure characterization by Tianhao Yan, Xiaomeng Xu, Jiafeng Liu, Yihuai Zhang, Muhammad Arif, Xiaowei Xu, Qiang Wang

“…The results demonstrate that the ML model based on the RF algorithm achieves the highest accuracy and best generalization capability in predicting permeability. …”
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Construction and Validation of Predictive Model to Identify Critical Genes Associated with Advanced Kidney Disease by Guangda Xin, Guangyu Zhou, Wenlong Zhang, Xiaofei Zhang

“…Differential expressed genes (DEGs) were identified and functional enrichment analysis. Machine learning algorithm-based prediction model was constructed to identify crucial functional feature genes related to ESRD. …”
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Predictive modelling of hospital-acquired infection in acute ischemic stroke using machine learning by Chun-Wei Chang, Chien-Hung Chang, Chia-Yin Chien, Jian-Lin Jiang, Tsai-Wei Liu, Hsiu-Chuan Wu, Kuo-Hsuan Chang

“…However, no existing model can specifically predict HAI in AIS patients. Therefore, we employed the Gradient Boosting matching learning algorithm to establish predictive models for HAI occurrence in AIS patients and poor 30-day functional outcomes (modified Rankin Scale > 2) in AIS patients with HAI by analyzing electronic health records from 6560 AIS patients. …”
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Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning by Jia Liu, Dan Zhu, Lingzhi Deng, Xiaoliang Chen

“…Objective We evaluated the predictive value of electrocardiogram (ECG)–derived features and developed an ML model to stratify HF risk. …”
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Analysis of the 10-day ultra-marathon using a predictive XG boost model by Beat Knechtle, Elias Villiger, David Valero, Lorin Braschler, Katja Weiss, Rodrigo Luiz Vancini, Marilia S. Andrade, Volker Scheer, Pantelis T. Nikolaidis, Ivan Cuk, Thomas Rosemann, Mabliny Thuany

“…A machine learning model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, country where the race takes place, the type of race and the kind of running surface. …”
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Benchmarking Variants of Recursive Feature Elimination: Insights from Predictive Tasks in Education and Healthcare by Okan Bulut, Bin Tan, Elisabetta Mazzullo, Ali Syed

“…Originally developed as an effective feature selection method in healthcare predictive analytics, Recursive Feature Elimination (RFE) has gained increasing popularity in Educational Data Mining (EDM) due to its ability to handle high-dimensional data and support interpretable modeling. …”
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Predictive factors of cardiovascular changes depending on the type and intensity of physical activity in professional athletes by N. P Garganeeva, I. F. Taminova, V. V. Kalyuzhin, E. V Kalyuzhina, I. N. Smirnova

“…To determine the early predictive factors of cardiovascular changes in professional athletes, depending on the type and intensity of physical activity.Material and methods. …”
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Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction by Xidi Yang, Liangyu Zhu, Wenyu Jiang, Yiting Yang, Mailin Gan, Linyuan Shen, Li Zhu

“…Furthermore, when the testing interval was set to 40 kg and further refined to the range of 50–90 kg, the model achieved an R² of 0.81 and a correlation of 0.90 for FCR prediction in the 30–105 kg range. Among the 19 machine learning algorithms tested, Gradient Boosting, LightGBM, and CatBoost showed superior performance, with Gradient Boosting achieving the best results. …”
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Maternal plasma cell-free RNA as a predictive test for fetal lung maturation by Sean W. D. Carter, Kay Yi Michelle Seah, Si En Poh, Winston Koh, Haruo Usuda, Erin L. Johnson, Yusaku Kumagai, Tsukasa Takahashi, Lara J. Monteiro, Reyna Peñailillo, Gino Nardocci, Hannah R. S. Watson, Masatoshi Saito, Mahesh A. Choolani, Sebastián E. Illanes, Matthew W. Kemp

“…Delivery and ventilation data were analyzed with ANOVA, Tukey HSD, and Dunnett T3 tests. A Random Forest algorithm identified genes that separated mature from immature fetal lung subgroups and determined AUC values for maternal and fetal cell-free RNA (cfRNA) feature sets to predict fetal lung maturation. …”
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Prediction of hearing aid cognitive outcomes in age-related hearing loss by Patrice Voss, Zaida Escila Martinez-Moreno, Francois Prévost, Anthony Zeitouni, Alejandro Lopez Valdes, Alejandro Lopez Valdes, Alejandro Lopez Valdes, Alejandro Lopez Valdes, Etienne de Villers-Sidani

“…We show that several factors can explain large portions of the variance observed in cognitive score changes following short-term hearing aid use in first-time users, suggesting that it might be possible to develop predictive algorithms to determine individualized estimates of the cognitive benefit of hearing aid use. …”
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Credit Scoring Prediction Using Deep Learning Models in the Financial Sector by Xi Shi, Dingfen Tang, Yike Yu

“…Although traditional statistical approaches provide foundational value, they frequently fall short when faced with the task of capturing the nuanced, non-linear associations embedded within extensive datasets, thereby limiting predictive precision. To address these shortcomings, we propose an innovative deep learning paradigm that effectively integrates structured financial information with unstructured behavioral data, thereby bolstering the reliability and accuracy of predictive analytics. …”
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Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes by Doljinsuren Enkhbayar, Jaehoon Ko, Somin Oh, Rumana Ferdushi, Jaesoo Kim, Jaehong Key, Erdenebayar Urtnasan

“…Advanced machine learning algorithms such as random forest, extreme gradient boosting, categorical boosting, and light gradient boosting machines were employed to train and validate the predictive AI models. …”
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