Validation of three models (Tolcher, Levine, and Burke) for predicting term cesarean section in Chinese population by Fangcan Sun, Minhong Shen, Bing Han, Youguo Chen, Fangfang Wu

“…Background: Some models predicting cesarean section (CS) have been proposed, with Tolcher, Levine, and Burke model well acknowledged. …”
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A Predictive Model for Secondary Posttonsillectomy Hemorrhage in Pediatric Patients: An 8‐Year Retrospective Study by Yuting Ge, Wenchuan Chang, Lixiao Xie, Yan Gao, Yue Xu, Huie Zhu

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A predictive model based on the GLIM diagnosis for malnutrition in older adult heart failure patients by Xinyu Tang, Changying Zhang, Feng Xiao, Fang Yang, Xiaoya Zhu, Yunlai Gao

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P2 Hybrid AMT Shift Clutch Engagement Control Strategy based on Model Prediction by Changqing Du, Huan Zhang, Xiliang Cao, Huawu Wang

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Explainable machine learning models predicting the risk of social isolation in older adults: a prospective cohort study by Mingfei Jiang, Xiaoran Li, YongLu

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Development and validation of risk prediction models for permanent hypocalcemia after total thyroidectomy in patients with papillary thyroid carcinoma by BoHan Cao, CanGang Zhang, MingMing Jiang, Yi Yang, XiCai Liu

Subjects: “…Predictive model…”
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Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study by Jiani Liu, Xin Zhang, Wei Li, Francis Manyori Bigambo, Dandan Wang, Xu Wang, Beibei Teng

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Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study by Wenting Feng, Wen Zhang, Yan Guo, Naixing Zhang, Liang Zhou, Dafeng Lin, Linlin Chen, Caiping Li, Liuwei Shi, Xiangli Yang, Peimao Li, Dianpeng Wang

“…Routine blood and biochemical indicators were extracted from the case data, and a range of machine learning algorithms including extreme gradient boosting (XGBoost) were employed to construct predictive models. The model underwent refinement to identify the most representative variables, and decision curve analysis was conducted to evaluate the net benefit of the model across various threshold levels.Primary outcome measures Model creation data set and validation data sets: ONIHL.Results The prediction model, developed using XGBoost, demonstrated exceptional performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.942, a sensitivity of 0.875 and a specificity of 0.936 on the validation data set. …”
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Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study by Eric Kamana, Jijun Zhao, Di Bai

“…The LSTMSeq2Seq model achieved an average prediction accuracy of 87.3%.Conclusions The LSTMSeq2Seq model significantly improved the prediction of malaria re-emergence based on the influence of climatic factors. …”
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An Analytical Cost Function Design and Implementation for Predictive Control of Induction Machine Drives by Wei Wei, Liming Yan, Shun Tian, Xisheng Xu, Keke Sun

Subjects: “…Model predictive control…”
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Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review by Guilherme Almeida, Fernanda Brito Correia, Ana Rosa Borges, Jorge Bernardino

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Prediction of the digestibility and digestible energy content of hay for horses using an enzymatic degradability method by D. Andueza, W. Martin-Rosset

“…The proposed models represent an alternative approach to the existing models for predicting the nutritive value and DE content of hay from their chemical composition.…”
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Bayesian compositional generalized linear mixed models for disease prediction using microbiome data by Li Zhang, Xinyan Zhang, Justin M. Leach, A. K. M. F. Rahman, Carrie R. Howell, Nengjun Yi

“…Abstract The primary goal of predictive modeling for compositional microbiome data is to better understand and predict disease susceptibility based on the relative abundance of microbial species. …”
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On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models by Youness El Mghouchi, Mihaela Tinca Udristioiu

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Fluorescence spectroscopic profiling of urine samples for predicting kidney transplant rejection by Zhe Yang, Minrui Zhang, Xianduo Li, Zhipeng Xu, Yi Chen, Xiaoyu Xu, Dongdong Chen, Lingquan Meng, Xiaoqing Si, Jianning Wang

“…The model successfully identified multiple rejection types with an average diagnostic accuracy of 95.56 %.Beyond proposing an innovative approach for predicting the risk of complications post-kidney transplantation, this study heralds the potential introduction of a non-invasive, rapid, and accurate supplementary method for risk assessment in clinical practice.…”
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Mathematical procedure for prediction of dental anxiety in children with inherited bleeding disorders by Vujkov Sanja, Cveticanin Livija, Bajkin Branislav, Blagojević Duška, Milekić Bojana, Vučinić Predrag

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Predicting Individual Systems Thinking Skills Using Bayesian Network Approach by Niamat Ullah Ibne Hossain, Raed Jaradat, Morteza Nagahi, Alex Gorod

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Comparative Analysis of Supervised Machine Learning Algorithms for COVID-19 Prediction by Rubina Shaheen, Beenish Akram, Amna Zafar, Talha Waheed

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Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets by Amin Salemnia, Seyedehmaryam Hosseini Boldaji, Vida Atashi, Manoochehr Fathi-Moghadam

“…This study emphasizes the importance of the Froude number in predicting jet behavior and shows the efficacy of advanced machine learning models in capturing complex fluid dynamics, providing valuable insights for optimizing engineering applications such as water jet cutting and cooling systems.…”
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Review and External Evaluation of Population Pharmacokinetic Models for Vedolizumab in Patients with Inflammatory Bowel Disease: Assessing Predictive Performance and Clinical Applicability by Marija Jovanović, Ana Homšek, Srđan Marković, Đorđe Kralj, Petar Svorcan, Tamara Knežević Ivanovski, Olga Odanović, Katarina Vučićević

“…The predictive performance of the models was assessed using prediction- and simulation-based diagnostics. …”
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