An ensemble model for predicting dyslipidemia using 3-years continuous physical examination data by Naiwen Zhang, Xiaolong Guo, Xiaxia Yu, Zhen Tan, Zhen Tan, Feiyue Cai, Feiyue Cai, Ping Dai, Jing Guo, Guo Dan

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Tweedie Model for Predicting Factors Associated with Distance Traveled to Access Inpatient Services in Kenya by Ngugi Mwenda, Mathew Kosgei, Gregory Kerich, Ruth Nduati

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A subtractive modelling approach for predicting the radiation of a cylindrical shell in a waveguide by Dumortier Florent, Kha Jamie, Karimi Mahmoud, Meyer Valentin, Maxit Laurent

“…Modeling the sound radiated from underwater structures immersed in various environments is necessary in ocean acoustics and naval engineering. …”
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An Explainable Multi-Model Stacked Classifier Approach for Predicting Hepatitis C Drug Candidates by Teuku Rizky Noviandy, Aga Maulana, Ghifari Maulana Idroes, Rivansyah Suhendra, Razief Perucha Fauzie Afidh, Rinaldi Idroes

“…To address this, we propose an explainable multi-model stacked classifier (MMSC) for predicting hepatitis C drug candidates. …”
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Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease by Manrong He, Chao Li, Wanxin Tang, Yingxi Kang, Yongdi Zuo, Yufang Wang

“…The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST. …”
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Development of an AI Model for Predicting Methacholine Bronchial Provocation Test Results Using Spirometry by SangJee Park, Yehyeon Yi, Seon-Sook Han, Tae-Hoon Kim, So Jeong Kim, Young Soon Yoon, Suhyun Kim, Hyo Jin Lee, Yeonjeong Heo

“…This study aimed to develop an artificial intelligence (AI) model to predict the MBPT results using forced expiratory volume in one second (FEV₁) and bronchodilator test measurements from spirometry. …”
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Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success by Ofri Bar, Stylianos Vagios, Omer Barkai, Joseph Elshirbini, Irene Souter, Jiawu Xu, Kaitlyn James, Charles Bormann, Makiko Mitsunami, Jorge E. Chavarro, Philipp Foessleitner, Douglas S. Kwon, Moran Yassour, Caroline Mitchell

“…Among them, MFI cases had higher diversity but lower inflammation than those with unexplained infertility. Our model showed the highest prediction accuracy at time point 2 of the IVF cycle. …”
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A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud by Zeyu Wang, Xiaofang Chen, Yiwei Wu, Linke Jiang, Shiming Lin, Gang Qiu

“…We then applied ensemble techniques, including Voting, Weighted, and Stacking methods, to combine different models and compare their performances. Feature interpretation was achieved through partial dependence plots (PDP), SHAP, and LIME, allowing us to understand each feature’s impact on the predictions. …”
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Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete by Muhammad Izhar Shah, Shazim Ali Memon, Muhammad Sohaib Khan Niazi, Muhammad Nasir Amin, Fahid Aslam, Muhammad Faisal Javed

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Explainable machine learning model for predicting compressive strength of CO2-cured concrete by Jia Chu, Bingbing Guo, Taotao Zhong, Qinghao Guan, Yan Wang, Ditao Niu

“…Herein, an explainable machine learning (ML) model was developed to predict the compressive strength of CO2-cured concrete. …”
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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

“…Methods To address these limitations, we develop an AI-based model to predict Nottingham grade from whole-slide images of hematoxylin and eosin (H&E)-stained breast cancer tissue using a pathology foundation model. …”
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Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery by Man Xu, Hao Liu, Anran Dai, Qilian Tan, Xinlong Zhang, Rui Ding, Chen Chen, Jianjun Zou, Yongjun Li, Yanna Si

“…Abstract Background Postoperative respiratory failure following cardiac surgery (CS-PRF) remains a critical complication with substantial morbidity and mortality. Current risk prediction models are limited by static assessments and suboptimal accuracy. …”
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A reproducible rat model for predicting incisional hernia recurrence: insights for clinical translation by Eric Toshiyuki Nakamura, Francisco Tustumi, Marina Alessandra Pereira, Leonardo Cardili, Ana Paula Gárate, Bruno Cogliati, Venancio Avancini Ferreira Alves, Sandra Nassa Sampietre, Cinthia Lanchotte Ferreira, Giovanna Mattos Ferreira, Michelly Moreira Campos, Ulysses Ribeiro Junior, Luiz Augusto Carneiro D’Albuquerque, Flávio Henrique Ferreira Galvão

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A Machine Learning Model for Predicting Prognosis in HCC Patients With Diabetes After TACE by Wu L, Chen L, Zhang L, Liu Y, Ouyang D, Wu W, Lei Y, Han P, Zhao H, Zheng C

“…Further, five predictive models were employed to establish prognosis models for 1-, 2-, and 3-year survival, respectively. …”
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Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks by Zakaria Elbayed, Abdelmjid Qadi EI Idrissi

“…This paper explores the application of deep neural networks (DNNs) as an alternative to the traditional Black–Scholes model for predicting European put option prices. …”
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Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma by Tianzhi Tang, Tianyu Guo, Bo Zhu, Qihui Tian, Yang Wu, Yefu Liu

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Machine Learning Model for Predicting Global Ionospheric TEC Maps Based on Constraint Conditions by Qingfeng Li, Hanxian Fang, Chao Xiao, Die Duan, Hongtao Huang, Ganming Ren

“…In this context, we propose a machine learning prediction model [predictive GAN variational autoencoder-label (PGVAE-label)] using a labeled graph of image segmentation as a constraint to predict the global ionospheric TEC. …”
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Predicting Deep Venous Thrombosis Using Artificial Intelligence: A Clinical Data Approach by Aurelian-Dumitrache Anghele, Virginia Marina, Liliana Dragomir, Cosmina Alina Moscu, Mihaela Anghele, Catalin Anghel

“…These models were rigorously tested using key metrics, including accuracy, precision, recall, F1-score, specificity, and receiver operating characteristic curve, to determine their effectiveness in clinical prediction. …”
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Neonatal apnea and hypopnea prediction in infants with Robin sequence with neural additive models for time series. by Julius Vetter, Kathleen Lim, Tjeerd M H Dijkstra, Peter A Dargaville, Oliver Kohlbacher, Jakob H Macke, Christian F Poets

“…Our prediction model presents a step towards an automatic prediction of neonatal apneas and hypopneas in infants at risk for upper airway obstruction. …”
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Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models by Pedro Torres-Bermeo, Kevin López-Eugenio, Carolina Del-Valle-Soto, Guillermo Palacios-Navarro, José Varela-Aldás

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