Nanostring-based multigene assay to predict recurrence for gastric cancer patients after surgery. by Jeeyun Lee, Insuk Sohn, In-Gu Do, Kyoung-Mee Kim, Se Hoon Park, Joon Oh Park, Young Suk Park, Ho Yeong Lim, Tae Sung Sohn, Jae Moon Bae, Min Gew Choi, Do Hoon Lim, Byung Hoon Min, Joon Haeng Lee, Poong Lyul Rhee, Jae J Kim, Dong Il Choi, Iain Beehuat Tan, Kakoli Das, Patrick Tan, Sin Ho Jung, Won Ki Kang, Sung Kim

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Explosion resistance evaluation and damage prediction of middle partition walls in prefabricated frame tunnels by Zhen Huang, Yuzhu Zhou, Ziming Xiong, Hao Lu, Minqian Sun, Maojiang Qin

“…The damage level of the middle partition wall was predicted by employing the deflection-span ratio damage assessment criterion and machine learning. …”
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Unraveling the role of gut microbiome in predicting adverse events in neoadjuvant therapy for rectal cancer by Jingxin Ma, Shengbo Sun, Xin Cheng, Cong Meng, Hanzheng Zhao, Wentao Fu, Yan Gao, Liyan Ma, Zhengyang Yang, Hongwei Yao, Jianrong Su

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Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adeeb Noor, Sufyan Ali Memon

“…Abstract Accurately predicting energy consumption in electric vehicles (EVs) is essential for enhancing energy efficiency and improving infrastructure planning. …”
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Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model. by Li Wang, Yu Zhang, Feng Li, Caiyun Li, Hongzeng Xu

“…Accurate mortality prediction of inpatient is crucial for clinical decision-making of non-ST-segment elevation myocardial infarction (NSTEMI) patients.…”
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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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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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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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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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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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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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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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Prediction of lung overdistension during mechanical ventilation using micro-RNA and gene expression by Cecilia López-Martínez, Paula Martín-Vicente, Laura Amado-Rodríguez, Inés López-Alonso, Margarita Fernández-Rodríguez, Adrián González-López, Pablo Martínez-Camblor, Juan Gómez, Andrew J. Boyle, Cecilia M. O’Kane, Daniel F. McAuley, James N. Tsoporis, Claudia dos Santos, Guillermo M. Albaiceta

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TelescopeML. II. Convolutional Neural Networks for Predicting Brown Dwarf Atmospheric Parameters by Ehsan (Sam) Gharib-Nezhad, Hamed Valizadegan, Natasha E. Batalha, Miguel J. S. Martinho, Ben W.P. Lew

“…Accurately and swiftly predicting the parameters of brown dwarf atmospheres from observational spectra is crucial for understanding their atmospheric composition and guiding future follow-up observations. …”
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Identification and validation of prognostic biomarkers in ccRCC: immune-stromal score and survival prediction by Fang Lyu, Yuxin Zhong, Qingliu He, Wen Xiao, Xiaoping Zhang

“…The risk score model exhibited a high degree of predictive accuracy for survival outcomes in ccRCC. …”
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