Stroke risk prediction: a deep learning approach for identifying high-risk patients by Afeez A. Soladoye, Kazeem M. Olagunju, Sunday A. Ajagbe, Ibrahim A. Adeyanju, Precious I. Ogie, Pragasen Mudali

“…The preprocessed dataset was used by GRU for prediction. The system gave average accuracy, Area Under Curve (AUC) and prediction time of 80.42%, 0.8940 and 0.678 s respectively. …”
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Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets by Tara Yousif Mawlood, Alla Ahmad Hassan, Rebwar Khalid Muhammed, Aso M. Aladdin, Tarik A. Rashid, Bryar A. Hassan

“…This study introduces a robust machine learning (ML) framework for predicting CVD risk by integrating two large, feature-identical datasets containing clinical and biological indicators along with patient history. …”
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An ensemble time-embedded transformer model for traffic conflict prediction at RRFB pedestrian crossings by Md Jamil Ahsan, Mohamed Abdel-Aty, B M Tazbiul Hassan Anik, Zubayer Islam

“…Fifty-two hours of video data were collected using portable CCTV cameras and analyzed using computer vision algorithms. A bounding box system was employed to predict vehicle conflict points and collision pairs. …”
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Deep learning-based time series prediction in multispectral and hyperspectral imaging for cancer detection by Lijun Hao, Changmin Wang, Jinshan Che, Mingming Sun, Yuhong Wang

“…Deep learning has recently been introduced to address these limitations, yet existing models often lack robust feature extraction, generalization capability, and effective domain adaptation strategies.MethodsIn this study, we propose a novel deep learning-based time series prediction framework for multispectral and hyperspectral medical imaging analysis. …”
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Ensemble Learning-Based Wine Quality Prediction Using Optimized Feature Selection and XGBoost by Sonam Tyagi, Ishwari Singh Rajput, Bhawnesh Kumar, Harendra Singh Negi

“…The study shows how feature selection improves wine quality prediction in different machine learning algorithms. …”
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A framework for predicting zoonotic hosts using pseudo-absences: the case of Echinococcus multilocularis by Andrea Simoncini, Dimitri Giunchi, Marta Marcucci, Alessandro Massolo

“…The predicted richness of intermediate hosts peaked in Central-Eastern Europe, Western North America and Central Asia, while the ratio of predicted hosts to total rodent richness was highest in the northern latitudes and the Tibetan Plateau. …”
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Predicting Nottingham grade in breast cancer digital pathology using a foundation model by Jun Seo Kim, Jeong Hoon Lee, Yousung Yeon, Doyeon An, Seok Jun Kim, Myung-Giun Noh, Suehyun Lee

“…Abstract Background The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide. …”
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Research on predicting the risk level of coal mine roof accident based on machine learning by Zhao-Yang Guan, Jin-Ling Xie, Shen-Kuang Wu, Chao Liang

“…Through comparison of model performance evaluation metrics, the Random Forest integration algorithm is introduced to improve the evaluation and prediction of the model, and the prediction accuracy jumps to 0.94. …”
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Predicting the time to get back to work using statistical models and machine learning approaches by George Bouliotis, M. Underwood, R. Froud

“…Objectives To compare model performance and predictive accuracy of classic regressions and machine learning approaches using data from the Inspiring Families programme. …”
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A Cluster Head Selection Algorithm for Extending Last Node Lifetime in Wireless Sensor Networks by Marcin Lewandowski, Bartłomiej Płaczek

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Prediction and Verification of Stability Domain in the Milling Process of Titanium Alloy Thin walled Workpiece by YUE Caixu, XIE Na, LI Xiaochen, LI Hengshuai, LIU Zhibo

“…During milling, chatter will lead to the increase of cutting force amplitude and of cutting force fluctuation, leading to the reduction of workpiece surface quality. In order to solve this problem, firstly, according to the flutter stability analysis algorithm, a dynamic milling system for titanium alloy Ti6Al4V thinwalled parts was established to obtain the stability lobe diagram. …”
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An interpretable disruption predictor on EAST using improved XGBoost and SHAP by D.M. Liu, X.L. Zhu, Y.S. Jiang, S. Wang, S.B. Shu, B. Shen, B.H. Guo, L.C. Liu

“…Based on the physical characteristics of the disruption, 2094 disruption shots and 4858 non-disruption shots from 2022 to 2024 were screened as training shots, and then the disruption prediction model was trained using the eXtreme Gradient Boosting (XGBoost) algorithm from training samples consisting of 16 diagnostic signals, such as plasma current, density, and radiation. …”
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Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study by Yubing Wang, Chao Qu, Jiange Zeng, Yumin Jiang, Ruitao Sun, Changlei Li, Jian Li, Chengzhi Xing, Bin Tan, Kui Liu, Qing Liu, Dianpeng Zhao, Jingyu Cao, Weiyu Hu

“…Using 12 machine learning algorithms, 110 combination predictive models were constructed. …”
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PREDICTION INTERVALS IN MACHINE LEARNING: RESIDUAL BOOTSTRAP AND QUANTILE REGRESSION FOR CASH FLOW ANALYSIS by Wa Ode Rahmalia Safitri, Farit Mochamad Afendi, Budi Susetyo

“…This study implements multivariate time series forecasting using gradient boosting algorithms (XGBoost, CatBoost, and LightGBM) to predict cash flow patterns in banking transactions, focusing on constructing reliable prediction intervals. …”
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Boosting grapevine phenological stages prediction based on climatic data by pseudo-labeling approach by Mehdi Fasihi, Mirko Sodini, Alex Falcon, Francesco Degano, Paolo Sivilotti, Giuseppe Serra

“…Predicting grapevine phenological stages (GPHS) is critical for precisely managing vineyard operations, including plant disease treatments, pruning, and harvest. …”
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Predicting outcomes following open abdominal aortic aneurysm repair using machine learning by Ben Li, Badr Aljabri, Derek Beaton, Leen Al-Omran, Mohamad A. Hussain, Douglas S. Lee, Duminda N. Wijeysundera, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Mohammed Al-Omran

“…However, there are no widely used tools to predict surgical risk in this population. We used machine learning (ML) techniques to develop automated algorithms that predict 30-day outcomes following open AAA repair. …”
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Development and validation of a nomogram for predicting low Kt/Vurea in peritoneal dialysis patients by Danfeng Zhang, Tian Zhao, Liting Gao, Huan Zhu, Haowei Jin, Guiling Liu, Deguang Wang

“…Abstract Background This study aimed to develop a nomogram to predict peritoneal dialysis (PD) adequacy in incident PD patients and identify those at high risk for low Kt/Vurea PD function. …”
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Ferroptosis-related genes in preeclampsia: integrative bioinformatics analysis, experimental validation and drug prediction by Lidan He, Feng Zhan, Xuemei Li, Huijuan Yang, Jianbo Wu

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Predicting and Preventing School Dropout with Business Intelligence: Insights from a Systematic Review by Diana-Margarita Córdova-Esparza, Juan Terven, Julio-Alejandro Romero-González, Karen-Edith Córdova-Esparza, Rocio-Edith López-Martínez, Teresa García-Ramírez, Ricardo Chaparro-Sánchez

“…The results highlight a wide range of predictive tools and methodologies, notably data visualization platforms (e.g., Power BI) and algorithms like decision trees, Random Forest, and logistic regression, demonstrating effectiveness in identifying dropout patterns and at-risk students. …”
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Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis by Maurilio Gustavo Nespeca, Rafael Rodrigues Hatanaka, Danilo Luiz Flumignan, José Eduardo de Oliveira

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