Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration by Heike Helmholz, Redon Resuli, Marius Tacke, Jalil Nourisa, Sven Tomforde, Roland Aydin, Regine Willumeit-Römer, Berit Zeller-Plumhoff

“…The generated data were utilized to develop regression models in order to assess and predict the cell response on Mg exposition in a concentration range of 2–20 mM Mg in cell culture medium extract. …”
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Predictive models for strength properties of sustainable mortars containing waste concrete powder: an empirical approach by Eric Ababio Ohemeng, Molusiwa S. Ramabodu, Nana-Addy Edward

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A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease by K. M. Tawsik Jawad, Anusha Verma, Fathi Amsaad, Lamia Ashraf

“…The goal of this research is to first develop an accurate ensemble model for prediction of unseen cases of CKD given the biomarkers. …”
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Development and validation of a risk assessment model for predicting the failure of early medical abortions: A clinical prediction model study based on a systematic review and meta-analysis. by An-Hao Liu, Bin Xu, Xiu-Wen Li, Yue-Wen Yu, Hui-Xin Guan, Xiao-Lu Sun, Yan-Zhen Lin, Li-Li Zhang, Xian-Di Zhuo, Jia Li, Wen-Qun Chen, Wen-Feng Hu, Ming-Zhu Ye, Xiu-Min Huang, Xun Chen

“…<h4>Objective</h4>As the first model in predicting the failure of early medical abortion (EMA) was inefficient, this study aims to develop and validate a risk assessment model for predicting the failure of EMAs more accurately in a clinical setting.…”
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A machine learning framework for predicting healthcare utilization and risk factors by Yead Rahman, Prerna Dua

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Machine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dots by Mehmet Sıddık Çadırcı, Musa Çadırcı

“…Although all models performed highly accurate results, SVR and NND demonstrated the best accurate property prediction by achieving excellent performance on the test and training datasets, with high R2, low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) metric values. …”
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Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam by Tuan Vu Dinh, Nhat-Duc Hoang, Xuan-Linh Tran

“…Hence, these results strongly confirm the efficacy of applying machine learning models for soil erosion prediction.…”
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Comparing the Performance of Regression and Machine Learning Models in Predicting the Usable Area of Houses with Multi-Pitched Roofs by Leszek Dawid, Anna Marta Barańska, Paweł Baran

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Predicting salinity levels in the Mekong delta (Viet Nam): analysis of machine learning and deep learning models by Phong Nguyen Duc, Thang Tang Duc, Giap Pham Van, Hoat Nguyen Van, Tuan Tran Minh

“…Our results prove that LSTM and XGB models have the best prediction. In particular, they showed good accuracy in predicting upstream salinity (RMSE: 0.25 to 0.30, R2 > 0.97) and downstream salinity (RMSE: 1.5 to 1.6, R2 > 0.88). …”
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Predicting Oncological and Functional Outcomes by Nephrectomy Type for T1 Renal Tumors Using Machine Learning Models by Dongrul Shin, Maisy Song, Jungyo Suh, Cheryn Song

“…Using machine learning algorithms, we aimed to develop a model to predict both outcomes simultaneously, according to each radical (RN) and partial nephrectomy (PN). …”
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Proteomic risk scores for predicting common diseases using linear and neural network models in the UK biobank by Alexander Smith, Paul Elliott, Manuel Mayr, Abbas Dehghan, Ioanna Tzoulaki

“…Proteomic risk scores demonstrated strong discrimination for most outcomes, with a C-index > 0.80 for 12 diseases. NN models outperformed linear models for 11 outcomes, particularly for diseases such as Parkinson’s disease (C-index 0.84) and pulmonary embolism (C-index 0.83), where nonlinear relationships contributed significantly to prediction. …”
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Predicting spread through air space of lung adenocarcinoma based on deep learning and machine learning models by Zengming Wang, Lingxin Kong, Bin Li, Qingtao Zhao, Xiaopeng Zhang, Huanfen Zhao, Wenfei Xue, Wei Li, Shun Xu, Guochen Duan

“…Abstract Objective The aim of this study was to develop a machine learning model that can predict spread through air space (STAS) of lung adenocarcinoma preoperatively. …”
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Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data by Yihang Zhou, Ruige Kong, Zhengsen Xu, Linlin Xu, Sibo Cheng

“…Employing a real‐world data set that includes nearly a decade of remote sensing data from California, U.S., these models predict the spread of wildfires for the following day. …”
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Machine learning models for predicting the bearing capacity of shallow foundations: A Comparative study and sensitivity analysis by Hamid Mohammadnezhad, Seyedmohammad Eslami

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Analyzing and Predicting the Agronomic Effectiveness of Fertilizers Derived from Food Waste Using Data-Driven Models by Ksawery Kuligowski, Quoc Ba Tran, Chinh Chien Nguyen, Piotr Kaczyński, Izabela Konkol, Lesław Świerczek, Adam Cenian, Xuan Cuong Nguyen

“…Random Forest and Cubist models showed strong generalization with high R² (0.79–0.83) for plant yield, while Cubist predicted IENU well in testing, with RMSE = 3.83 and R² = 0.78. …”
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Accuracy of the 'Benish' and 'Developed Benish' Models in Predicting the Probability of Restating Financial Statements in Iran's Economic Environment by Sasan Mehrani, Akbar Rahimipoor

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PREDICTING STOCK PRICE DIRECTION OF EUROZONE BANKS: CAN DEEP LEARNING TECHNIQUES OUTPERFORM TRADITIONAL MODELS? by Bogdan Ionuț ANGHEL

“…This study compares the predictive performance of Bidirectional Long Short-Term Memory (BiLSTM) and Long Short Term Memory (LSTM) with traditional models - Extreme Gradient Boosting (XGBoost) and Logistic Regression - in predicting the daily stock price direction of the ten largest Eurozone banks by market capitalization. …”
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Comparative artificial neural network models for predicting kinetic parameters of biomass pyrolysis from the biomass characteristics by Kiattikhoon Phuakpunk, Benjapon Chalermsinsuwan, Suttichai Assabumrungrat

“…Overall, these ANN models demonstrated a reliable and cost-effective alternative to conventional TGA methods for predicting the kinetic parameters of biomass pyrolysis.…”
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Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models by In-Hwan Kim, Jiheon Jeong, Jun-Sik Kim, Jisup Lim, Jin-Hyoung Cho, Mihee Hong, Kyung-Hwa Kang, Minji Kim, Su-Jung Kim, Yoon-Ji Kim, Sang-Jin Sung, Young Ho Kim, Sung-Hoon Lim, Seung-Hak Baek, Jae-Woo Park, Namkug Kim

“…GPOSC-Net consists of two key components: a landmark prediction model that estimates post-surgical cephalometric changes and a latent diffusion model that generates realistic synthesizes post-operative lateral cephalograms images based on predicted landmarks and segmented profile lines. …”
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Predicting Postoperative Progression of Ossification of the Posterior Longitudinal Ligament in the Cervical Spine Using Interpretable Radiomics Models by Siyuan Qin, Ruomu Qu, Ke Liu, Ruixin Yan, Weili Zhao, Jun Xu, Enlong Zhang, Feifei Zhou, Ning Lang

“…Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. …”
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