Enhanced Fall Detection and Prediction Using Heterogeneous Hidden Markov Models in Indoor Environment by Oumaima Guendoul, Hamd Ait Abdelali, Youness Tabii, Rachid Oulad Haj Thami, Omar Bourja

“…The model achieved a 61% prediction rate with an accuracy of 81.51%. …”
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Risk prediction models for sarcopenia in elderly people: a systematic review and meta-analysis by Jie Yin, Yiyong Xu, Mian Cai, Xiwei Fang

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Systematic review of risk prediction models for arteriovenous fistula dysfunction in maintenance hemodialysis patients. by Shiyan Yao, Guannan Ma, Yongze Dong, Mengjiao Zhao, Luchen Chen, Wenhao Qi, Huajuan Shen

“…Although various models for predicting AVF risk have emerged, a comprehensive review of their advancements and challenges is currently lacking. …”
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Predicting high confidence ctDNA somatic variants with ensemble machine learning models by Rugare Maruzani, Liam Brierley, Andrea Jorgensen, Anna Fowler

“…We built two Random Forest (RF) models for predicting high confidence somatic ctDNA variants in low and high depth cfDNA NGS data. …”
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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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Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy by Guangzong Li, Yuesen Zhang, Di Li, Manhong Zhao, Lin Yin

“…The Extra Trees model demonstrated the highest predictive accuracy. …”
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Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards by Tünde Takáts, László Pásztor, Mátyás Árvai, Gáspár Albert, János Mészáros

“…Our results indicate that ML models can feasibly replace the empirical USLE model for erosion prediction. …”
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A systematic review of risk prediction models for post-stroke acute kidney injury by YANG Li, QIN Qin, LIU Handan, LI Huiming, WEI Xuemei, CUI Lijun

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Prediction models for patients with esophageal or gastric cancer: A systematic review and meta-analysis. by H G van den Boorn, E G Engelhardt, J van Kleef, M A G Sprangers, M G H van Oijen, A Abu-Hanna, A H Zwinderman, V M H Coupé, H W M van Laarhoven

“…<h4>Background</h4>Clinical prediction models are increasingly used to predict outcomes such as survival in cancer patients. …”
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Prediction models after hepatectomy for hepatocellular carcinoma-based ultrasonic radiomics: an observational study by Dong Jiang, Jialun Ren, Yi Qian, Yijun Gu, Ru Wang, Hua Yu, Hui Dong, Dongyu Chen, Yan Chen, Haozheng Jiang, Yiran Li

“…Abstract Background This study aims to develop and validate predictive models for postoperative complications and early recurrence in hepatocellular carcinoma (HCC) patients. …”
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Risk models to predict chronic kidney disease and its progression: a systematic review. by Justin B Echouffo-Tcheugui, Andre P Kengne

“…Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use.…”
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Comparative Analysis of Neural Network Models for Predicting Battery Pack Safety in Frontal Collisions by Jun Wang, Ouyang Chen, Zhenfei Zhan, Zhiwei Zhao, Huanhuan Bao

“…Finally, the prediction accuracy of the models was compared based on error functions. …”
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Reducing Judicial Inconsistency through AI: A Review of Legal Judgement Prediction Models by Wu Yifan

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Neutrosophic Statistical Regression Models for Predicting the Incidence of Nosocomial Infections in Post-Trauma Patients by Nairovys Gómez Martinez, Gerardo Ramos Serpa, Riber Fabian Donoso Noroña, Gloria Rebeca Medina Naranjo

Subjects: “…regression models…”
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Prediction Models with Multiple Linear Regression for Improving Acoustic Performance of Textile Industry Plants by Muammer YAMAN, Cüneyt KURTAY, Gülsu ULUKAVAK HARPUTLUGIL

“…The acoustic analyses of the scenario plants were performed in the ODEON Auditorium, and A-weighted sound pressure level (LA), noise reduction (NR), and reverberation time (RT) were determined. From the data, prediction equations were created with a multiple linear regression (MLR) model. …”
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Comparative analysis of supervised learning models for effluent quality prediction in wastewater treatment plants by Liu Bo-qi, Zhou Ding-jie, Zhao Yang, Shi Long-yu

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Prediction Models for Postoperative Delirium of Cardiovascular Surgery (PODOCVS): Protocol for a Systematic Review by Xuling Zhao, Yike Wang, Liju Li, Meijuan Lan, Xiaodi He

“…Two researchers (ZXL and WYK) will independently extract the data and assess the included studies’ model quality using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and the Predictive Model Bias Risk Assessment Tool (PROBAST). …”
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Winter Wheat Nitrogen Content Prediction and Transferability of Models Based on UAV Image Features by Jing Zhang, Gong Cheng, Shaohui Huang, Junfang Yang, Yunma Yang, Suli Xing, Jingxia Wang, Huimin Yang, Haoliang Nie, Wenfang Yang, Kang Yu, Liangliang Jia

“…This study aims to present an innovative approach by integrating 40 texture features and 22 spectral features from UAV multispectral images with machine learning (ML) methods (RF, SVR, and XGBoost) for winter wheat nitrogen content prediction. In addition, through analysis of an 8-year long-term field experiment with rigorous data, the results indicated that (1) the RF and XGboost models incorporating both spectral and texture features achieved good prediction accuracy, with R² values of 0.98 and 0.99, respectively, RMSE values of 0.10 and 0.07, and MAE values of 0.07and 0.05; (2) models trained on Farmers’ Practice (FP) data showed superior transferability to Ecological Intensification (EI) conditions (R² = 0.98, RMSE = 0.08, and MAE = 0.05 for XGBoost), while EI-trained models performed less well when applied to FP conditions (R² = 0.89, RMSE = 0.45, and MAE = 0.35 for XGBoost). …”
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Leveraging Large Language Models for Predicting Postoperative Acute Kidney Injury in Elderly Patients by Hanfei Zhu, Ruojiang Wang, Jiajie Qian, Yuhao Wu, Zhuqing Jin, Xishen Shan, Fuhai Ji, Zixuan Yuan, Tingrui Pan

“…Objective: The objective of this work is to develop a framework based on large language models (LLMs) to predict postoperative acute kidney injury (AKI) outcomes in elderly patients. …”
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Monitoring and predicting cotton leaf diseases using deep learning approaches and mathematical models by Abdul Rehman, Nadeem Akhtar, Omar H. Alhazmi

“…We consequently used deep learning models to predict cotton diseases, i.e., Aphids, Armyworms, Bacterial Blight, Powdery Mildew, Target Spot, and Healthy leaf. …”
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