Identify the underlying true model from other models for clinical practice using model performance measures by Yan Li

Subjects: “…Clinical risk prediction model…”
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Evaluating the Accuracy of Land-Use Change Models for Predicting Vegetation Loss Across Brazilian Biomes by Macleidi Varnier, Eliseu José Weber

“…Land-use change models are used to predict future land-use scenarios. …”
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Prediction of chicken gender before putting eggs in incubator using logistic regression model by Abbas Parchami, Ramin Aghaei Afshar, Erfan Fadaie, Hasan Ghaemi, Ehsanollah Sakhaee

“…This paper propose several regression models to predict the probability of hatch a cockerel from egg before it is even putted in an incubator. …”
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Network traffic nonlinear prediction combined with mutifractal by WANG Sheng-hui, QIU Zheng-ding

Subjects: “…traffic modeling…”
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Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber by T. Nageshkumar, Prateek Shrivastava, L. Ammayapan, Manisha Jagadale, L. K. Nayak, D. B. Shakyawar, Indran Suyambulingam, P. Senthamaraikannan, R. Kumar

“…Machine learning model coupled with graphical user interface was developed to predict mechanical properties of flax fiber. …”
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Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction by Chenglong Guo, Binyu Gao, Xuexue Han, Tianxing Zhang, Tianqi Tao, Jinggang Xia, Honglei Liu

“…This study aimed to develop an interpretable artificial intelligence (AI) model for HF severity prediction using multidimensional clinical data. …”
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Comparative Analysis of Artificial Neural Networks with Classical Regression Models for Predicting Dissolved Oxygen in Water by Ana Ivette Jater Ruiz, Francisco Primero Primero, Roberto Alejo Eleuterio, Francisco Javier Illescas Martínez, Federico Del Razo López, Everardo Granda

“…In this study, we evaluate the effectiveness of Artificial Neural Networks (ANNs) in predicting DO levels by comparing seven different ANN architectures to nine classical regression models. …”
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Hybrid Machine Learning Model for Predicting the Fatigue Life of Plain Concrete Under Cyclic Compression by Lucas Rodrigues Lunardi, Paulo Guilherme Cornélio, Lisiane Pereira Prado, Caio Gorla Nogueira, Emerson Felipe Felix

“…This study introduces a hybrid machine learning model based on the stacking ensemble strategy, integrating Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANNs) to enhance prediction accuracy. …”
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Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review by Morris Ogero, Samuel Akech, Mike English, Jalemba Aluvaala, Ambrose Agweyu, Lucas Malla, Rachel Jelagat Sarguta, Nelson Owuor Onyango

“…Objectives To identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).Design Systematic review of peer-reviewed journals.Data sources MEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.Eligibility criteria We included model development studies predicting in-hospital paediatric mortality in LMIC.Data extraction and synthesis This systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. …”
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PBertKla: a protein large language model for predicting human lysine lactylation sites by Hongyan Lai, Diyu Luo, Mi Yang, Tao Zhu, Huan Yang, Xinwei Luo, Yijie Wei, Sijia Xie, Feitong Hong, Kunxian Shu, Fuying Dao, Hui Ding

“…Results Here, we proposed a novel human Kla site predictor (named PBertKla) through curating a reliable benchmark dataset with proper sample length and sequence identity threshold to train a protein large language model with optimal hyperparameters. Extensive experimental results consistently demonstrated that our model possessed robust human Kla site prediction ability, achieving an AUC (area under receiver operating characteristic curve) value of over 0.880 on the independent validation data. …”
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Comparative analysis of machine learning models for predicting water quality index in Dhaka’s rivers of Bangladesh by Mosaraf Hosan Nishat, Md. Habibur Rahman Bejoy Khan, Tahmeed Ahmed, Syed Nahin Hossain, Amimul Ahsan, M. M. El-Sergany, Md. Shafiquzzaman, Monzur Alam Imteaz, Mohammad T. Alresheedi

“…To our knowledge, this is the first study to apply such a comprehensive range of ML models to predict the WQI of Dhaka’s four major rivers. …”
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Explainable machine learning model for predicting the transarterial chemoembolization response and subtypes of hepatocellular carcinoma patients by Yunjie Zhang, Songjian Tong, Junhui Yang, Jiawei Lin, Yifan Kong, Deyu Lu, Yan Chen, Yingchao Li, Linfeng Xu, Xiuyan Kong, Guoqing Zhu, Hao Zhang, Pixu Liu, Zhijie Yu, Jinglin Xia

“…Through recursive feature elimination (RFE), we systematically developed prediction models employing ten distinct ML algorithms. …”
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Developing a Modelling Approach for Predicting PM2.5 Concentrations in Classrooms for School Children by Chun-Yu Chen, Chien-Yu Hsu, Gurumurthy Ramachandran, Thanawat Khajonklin, Chungsik Yoon, Perng-Jy Tsai

“…No significant differences were observed between measured and model-predicted PM2.5 levels inside the classroom. …”
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The possibilities of predicting the individual risk of cervical cancer in women of reproductive age using mathematical modeling by L. A. Klyukina, E. A. Sosnova, A. A. Ishchenko, M. M. Davydov

“…This, in turn, determines the need for a thorough search, study and analysis of possible cofactors that can lead to neoplastic changes in the cervix.Aim. To develop a model for predicting the individual risk of CC in women of reproductive age, taking into account clinical, anamnestic, laboratory and histological data.Materials and methods. …”
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Predicting changes in land use and land cover using remote sensing and land change modeler by Brijmohan Bairwa, Rashmi Sharma, Arnab Kundu, Saad Sh. Sammen, Fahad Alshehri, Chaitanya Baliram Pande, Chaitanya Baliram Pande, Zoltan Orban, Ali Salem, Ali Salem

“…This research is centered on the modeling of spatio-temporal trajectories of landscape transformation spanning from 1988 to 2018, with a forward-looking scenario up to 2040. …”
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A Comparative Analysis of the Effectiveness of Multiple Models for Predicting Heart Failure using Data Mining by Ahmed Sami Jaddoa, Juliet Kadum, Amaal Kadum

“…For forecasting, decision-making, and disease prediction, DM technologies are essential. This research predicts heart disease using DM algorithms. …”
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Selection of an Optimal Metabolic Model for Accurately Predicting the Hepatic Clearance of Albumin-Binding-Sensitive Drugs by Ren-Jong Liang, Shu-Hao Hsu, Hsueh-Tien Chen, Wan-Han Chen, Han-Yu Fu, Hsin-Ying Chen, Hong-Jaan Wang, Sung-Ling Tang

“…Although numerous models have been developed to estimate drug dosage, some may fail to predict liver drug clearance owing to inappropriate hepatic clearance models during IVIVE. …”
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Interpretable machine learning models for predicting childhood myopia from school-based screening data by Qi Feng, Xin Wu, Qianwen Liu, Yuanyuan Xiao, Xixing Zhang, Yan Chen

“…Abstract This study assessed the efficacy of various diagnostic indicators and machine learning (ML) models in predicting childhood myopia. A total of 2,365 children aged 5–12 years were included in the study. …”
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A novel cuproptosis-associated LncRNA model predicting prognostic and immunotherapy response for glioma by Bo Lei, Ao zhan, Guoliang You, Honggang Wu, Shu Chen, Daobao Zhang, Zhiye Liu, Niandong Zheng

“…A total of 10 cuproptosis-associated lncRNAs were selected to construct a prognostic prediction model. The high-risk group was associated with poor overall survival (OS) and progression-free survival (PFS). …”
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Development and validation of machine learning models predicting hospitalizations of hypertensive patients over 12 months by A. E. Andreychenko, A. D. Ermak, D. V. Gavrilov, R. E. Novitsky, O. M. Drapkina, A. V. Gusev

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