Comparative Analysis of Resampling Techniques for Class Imbalance in Financial Distress Prediction Using XGBoost by Guodong Hou, Dong Ling Tong, Soung Yue Liew, Peng Yin Choo

“…This study examines eight resampling techniques for improving distress prediction using the XGBoost algorithm. The study was performed on a dataset acquired from the CSMAR database, containing 26,383 firm-quarter samples from 639 Chinese A-share listed companies (2007–2024), with only 12.1% of the cases being distressed. …”
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Enhancing stroke-associated pneumonia prediction in ischemic stroke: An interpretable Bayesian network approach by Xingyu Liu, Jiali Mo, Zuting Liu, Yanqiu Ge, Tian Luo, Jie Kuang

“…This study aims to develop an interpretable Bayesian network (BN) model for predicting SAP in IS patients, focusing on enhancing both predictive accuracy and clinical interpretability. …”
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Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity by Nasir Khan, Mehdi Razavifar, Qazi Adnan Ahmad, Muhammad Siyar, Masoud Riazi, Waqar Khan, Jafar Qajar

“…In this study, we develop a machine learning-based algorithm to rigorously predict the synergistic effects of ultrasonication and solvation on crude oil viscosity. …”
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SGO enhanced random forest and extreme gradient boosting framework for heart disease prediction by Anima Naik, Ghanshyam G. Tejani, Seyed Jalaleddin Mousavirad

“…This study proposes a heart disease prediction (HDP) model employing Random Forest (RF) and eXtreme Gradient Boosting (XGB) classifiers. …”
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Rapid Damage Assessment and Bayesian-Based Debris Prediction for Building Clusters Against Earthquakes by Xiaowei Zheng, Yaozu Hou, Jie Cheng, Shuai Xu, Wenming Wang

“…Finally, with the structural response data of maximum floor displacement, a surrogate model for rapidly calculating seismic responses of structures is developed based on the XGBoost algorithm, achieving R² > 0.99 for floor displacements and R² = 0.989 for maximum inter-story drift ratio (MIDR) predictions. …”
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Artificial Intelligence-Based Prediction of Bloodstream Infections Using Standard Hematological and Biochemical Markers by Ferhat DEMİRCİ, Murat AKŞİT, Aylin DEMİRCİ

“…Basophil count, while ranked highest by SHAP, showed low sensitivity, highlighting the difference between algorithmic weight and bedside utility. Conclusion: These findings support the integration of routine, readily available laboratory data into an explainable AI framework to accurately predict culture positivity. …”
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Artificial intelligence models utilize lifestyle factors to predict dry eye related outcomes by Andrew D. Graham, Jiayun Wang, Tejasvi Kothapalli, Jennifer E. Ding, Helen Tasho, Alisa Molina, Vivien Tse, Sarah M. Chang, Stella X. Yu, Meng C. Lin

“…Abstract The purpose of this study is to examine and interpret machine learning models that predict dry eye (DE)-related clinical signs, subjective symptoms, and clinician diagnoses by heavily weighting lifestyle factors in the predictions. …”
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Building a machine learning-based risk prediction model for second-trimester miscarriage by Sangsang Qi, Shi Zheng, Mengdan Lu, Aner Chen, Yanbo Chen, Xianhu Fu

“…Currently, there is a scarcity of research on predictive models for the risk of second-trimester miscarriage. …”
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Predicting the infecting dengue serotype from antibody titre data using machine learning. by Bethan Cracknell Daniels, Darunee Buddhari, Taweewun Hunsawong, Sopon Iamsirithaworn, Aaron R Farmer, Derek A T Cummings, Kathryn B Anderson, Ilaria Dorigatti

“…Despite these challenges, the best performing machine learning algorithm achieved 76.3% (95% CI 57.9-89.5%) accuracy on the out-of-sample test set in predicting the infecting serotype from PRNT data. …”
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Prediction of Electrotactile Stimulus Threshold in Real Time Using Voltage Waveforms Between Electrodes by Vibol Yem, Yasushi Ikei, Hiroyuki Kajimoto

“…In this study, we explored four methods to predict the electrotactile sensation threshold across all five fingers. …”
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Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers by Hongqi Zhou, Weiyun Jin, Lindi Li, Xiangwen Nie, Weiwei Wu, Ran Chen, Qizhen Xie, Haixia Wu, Weiwei Jiang, Min Tang, Jinhai Wang, Maoyuan Wang

“…Abstract This study aims to develop a multidimensional risk prediction model, identify characteristic inflammation-nutrition biomarkers, and optimize clinical decision-making. …”
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Evaluation of multiple machine learning models predicting the results of hybrid imaging in primary hyperparathyroidism by Anna Drynda, Jacek Podlewski, Karolina Kucharczyk, Grzegorz Sokołowski, Anna Sowa-Staszczak, Alicja Hubalewska-Dydejczyk, Małgorzata Trofimiuk- Müldner

“…MATERIAL AND METHODS: Development and evaluation of logistic regression (LR), classification trees utilizing the classification and regression trees (CART) algorithm, random forest (RF), and boosted trees employing XGBoost (XGB) predictive models. …”
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Analysis and prediction of land use changes: the case study of coastal areas of Gilan province by Sahar Abdollahi, Hashem Dadashpoor

“…Land use changes and then modeling the transmission potential were explored using multilayer perceptron algorithm of artificial neural network using 13 independent variables and obtained 7 sub-models for modeling land use change for 2016 and then using Markov chain method, land use map for the year 2016 was predicted with a coefficient of Kappa 0.98. …”
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ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

“…Utilizing the high-quality data processed by this model, this study proposes and constructs a novel Grey Wolf Optimizer and Bidirectional Long Short-Term Memory (GWO-BiLSTM) temperature prediction framework, which combines a Grey Wolf Optimizer (GWO)-enhanced algorithm with a Bidirectional Long Short-Term Memory (BiLSTM) network. …”
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Prediction of microbe-drug associations using a CNN-Bernoulli random forest model by Zihao Song, Qingnuo Li, Jincheng Zhao, Qinggang Bu, Zekang Bian, Jia Qu

“…This approach enhances computational efficiency and improves the model’s ability to capture complex patterns, thereby increasing the precision and interpretability of drug response predictions. The dual use of the Bernoulli distribution in BRF ensures algorithmic consistency and contributes to superior performance. …”
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Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women by Peng Wang, Qiang Yin, Kangzhi Ding, Huaichang Zhong, Qundi Jia, Zhasang Xiao, Hai Xiong

“…Abstract The aim of this study was to establish the optimal prediction model by comparing the prediction effect of 6 kinds of prediction models containing biochemical indexes on the risk of osteoporosis in middle-aged and elderly women in Tibet. …”
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Predicting ESWL success for ureteral stones: a radiomics-based machine learning approach by Ran Yang, Dan Zhao, Chunxue Ye, Ming Hu, Xiao Qi, Zhichao Li

“…Abstract Objectives This study aimed to develop and validate a machine learning (ML) model that integrates radiomics and conventional radiological features to predict the success of single-session extracorporeal shock wave lithotripsy (ESWL) for ureteral stones. …”
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CacPred: a cascaded convolutional neural network for TF-DNA binding prediction by Shuangquan Zhang, Anjun Ma, Xuping Xie, Zhichao Lian, Yan Wang

“…In recent years, convolutional neural networks (CNNs) have succeeded in TF-DNA binding prediction, but existing DL methods’ accuracy needs to be improved and convolution function in TF-DNA binding prediction should be further explored. …”
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Weighted Hybrid Random Forest Model for Significant Feature prediction in Alzheimer’s Disease Stages by M. Rohini, D. Surendran

“…Abstract In recent studies, several machine learning and deep learning prediction models have been proposed for the early detection and classification of various stages of Alzheimer’s Disease (AD). …”
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On Usage of Artificial Intelligence for Predicting Neonatal Diseases, Conditions, and Mortality: A Bibliometric Review by Flavio Leandro de Morais, Raysa Carla Leal da Silva, Anna Beatriz Silva, Estefani Pontes Simao, Maria Eduarda Ferro de Mello, Stephany Paula da Silva Canejo, Katia Maria Mendes, Waldemar Brandao Neto, Jackson Raniel Florencio da Silva, Maicon Herverton Lino Ferreira da Silva Barros, Patricia Takako Endo

“…The literature presents artificial intelligence models as promising tools to assist healthcare professionals in disease prediction and support clinical decision-making. Methods: This study conducts a bibliometric review of the use of artificial intelligence models in predicting neonatal diseases, conditions and mortality. …”
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