Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study by Shengyu Liu, Anran Wang, Xiaolei Xiu, Ming Zhong, Sizhu Wu

“…The macrofactors affecting model performance were also screened using the multilevel factor elimination algorithm. …”
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Integrating machine learning and multi-omics analysis to unveil key programmed cell death patterns and immunotherapy targets in kidney renal clear cell carcinoma by Fanyan Ou, Yi Pan, Qiuli Chen, Lixiong Zeng, Kanglai Wei, Delin Liu, Qian Guo, Liquan Zhou, Jie Yang

“…We utilized a combination of 101 machine learning algorithms to analyze the TCGA-KIRC cohort and the GSE22541 KIRC patients, screening for cell death patterns closely associated with prognosis from 18 potential modes. …”
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A risk prediction model for gastric cancer based on endoscopic atrophy classification by Yadi Lan, Weijia Sun, Shen Zhong, Qianqian Xu, Yining Xue, Zhaoyu Liu, Lei Shi, Bing Han, Tianyu Zhai, Mingyue Liu, Yujing Sun, Hongwei Xu

“…We employed the Least absolute shrinkage and selection operator (LASSO) to screen variables for the LR model. However, we chose all the variables to construct the models for other machine learning algorithms. …”
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Prediction of postpartum depression in women: development and validation of multiple machine learning models by Weijing Qi, Yongjian Wang, Yipeng Wang, Sha Huang, Cong Li, Haoyu Jin, Jinfan Zuo, Xuefei Cui, Ziqi Wei, Qing Guo, Jie Hu

“…Seven feature selection methods and six ML algorithms were employed to develop models, and their prediction performances were compared. …”
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Machine Learning Models in the Detection of MB2 Canal Orifice in CBCT Images by Shishir Shetty, Meliz Yuvali, Ilker Ozsahin, Saad Al-Bayatti, Sangeetha Narasimhan, Mohammed Alsaegh, Hiba Al-Daghestani, Raghavendra Shetty, Renita Castelino, Leena R David, Dilber Uzun Ozsahin

“…The highest precision (86.8%) and recall (92.5%) was observed with the SVM model. Conclusion: The success rates (AUC, precision, recall) of ML algorithms in the detection of MB2 were remarkable in our study. …”
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A Credible Monitoring Model for Carbon Emissions in Industrial Parks Based on Blockchain Technology by Dong WANG, Jingli FENG, Da LI, Jingwei NIU, Jun LI

“…Finally, LOF algorithm is used to detect long-period abnormal outliers of index data, which can solve the problem of data distortion or self-screening of misstatement to some extent.…”
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Breast mass lesion area detection method based on an improved YOLOv8 model by Yihua Lan, Yingjie Lv, Jiashu Xu, Yingqi Zhang, Yanhong Zhang

“…The YOLOv8-GHOST model incorporates GHOSTConv and C3GHOST modules into the original YOLOv8 model to capture richer feature information while using only 57% of the number of parameters required by the original model. …”
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A multi-feature fusion exercise recommendation model based on knowledge tracing machines by ZHUGE Bin, WANG Ying, XIAO Mengfan, YAN Lei, WANG Bingyan, DONG Ligang, JIANG Xian

“…To address these issues, combining the knowledge tracing machine and the user-based collaborative filtering algorithm, as a KTM-based multi-feature fusion exercise recommendation model, SKT-MFER was proposed. …”
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Cuproptosis-related lncRNA predicts prognosis and immune pathways in osteosarcoma patients by LIAO Jun, FENG Yanbin, XI Deshuang, ZONG Shaohui

“…Objective To identify cuproptosis-related lncRNAs (CRLs) and use them to construct models to predict survival in osteosarcoma (OS) patients. …”
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Enhancing Daylight and Energy Efficiency in Hot Climate Regions with a Perforated Shading System Using a Hybrid Approach Considering Different Case Studies by Basma Gaber, Changhong Zhan, Xueying Han, Mohamed Omar, Guanghao Li

“…A hybrid approach integrating parametric modeling, machine learning, multi-criteria decision-making (MCDM), and genetic algorithm (GA) is used to optimize the design incorporating architects’ preferences. …”
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A predictive model of cognitive impairment in Parkinson's disease based on multivariate logistic regression by BA Mengru, YIN Xiaohong, LI Shaoyuan

“…First, the least absolute shrinkage and selection operator (LASSO) algorithm was applied to analyze the risk factors that may affect the cognitive ability of patients, and the clinical variables with high correlation were screened out. …”
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Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network by Xiaorong Li, Lijun Zhang, Dongchen Li, Dan Guo

“…In this paper, RNN (Recurrent Neural Network) algorithm is used to conduct an in-depth analysis of HR strategic decision-making and an HR strategic decision model is constructed for simulation. …”
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Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers by Hongqi Zhou, Weiyun Jin, Lindi Li, Xiangwen Nie, Weiwei Wu, Ran Chen, Qizhen Xie, Haixia Wu, Weiwei Jiang, Min Tang, Jinhai Wang, Maoyuan Wang

“…All patients were followed until death or a uniform administrative censoring point.LASSO logistic regression was employed to model the outcome as a binary classification (death within 1 year: yes/no).This study employed a small-sample modeling approach, initially using LASSO regression for feature selection and dimensionality reduction, followed by variance inflation factor and collinearity screening for secondary feature selection. …”
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Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women by Peng Wang, Qiang Yin, Kangzhi Ding, Huaichang Zhong, Qundi Jia, Zhasang Xiao, Hai Xiong

“…In test set, the order of AUC from highest to lowest is XGB (0.848), regression (0.801), Random Forest (0.772), SVM (0.755), OSTA (0.739), ANN (0.732). SVM and XGB algorithm models had better screening effect on osteoporosis than OSTA in middle-aged and elderly Tibetan residents in Tibet. …”
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Discovering New Tyrosinase Inhibitors by Using In Silico Modelling, Molecular Docking, and Molecular Dynamics by Kevin A. OréMaldonado, Sebastián A. Cuesta, José R. Mora, Marcos A. Loroño, José L. Paz

“…<b>Methods</b>: Four machine learning algorithms and topographical descriptors were tested for model construction. …”
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External Validation of Persistent Severe Acute Kidney Injury Prediction With Machine Learning Model by Simone Zappalà, PhD, Francesca Alfieri, MS, Andrea Ancona, PhD, Antonio M. Dell’Anna, MD, Kianoush B. Kashani, MD, MS

“…The performance of the PersEA model, a boosted tree algorithm fed by hourly patient data via electronic health records to provide real-time psAKI predictions, was evaluated using specific metrics that penalize late alarms. …”
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Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin by Pei Zhang, Qiong Chen, Jiahui Lao, Juan Shi, Jia Cao, Xiao Li, Xin Huang

“…Univariate analyses and the least absolute shrinkage and selection operator algorithm were used to screen risk factors and construct the model. …”
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Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records by Aamna AlShehhi, Hiba Alblooshi, Ruba Fadul, Natnael Tumzghi, Amal Al Tenaiji, Mariam Al Harbi, Fatma Al-Jasmi

“…Using nested cross-validation, we trained different feature selection algorithms in combination with various ML algorithms and evaluated their performance with multiple evaluation metrics. …”
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Nomogram for predicting the severity of high-risk plaques in acute coronary syndrome by Miao-Na Bai, Miao-Na Bai, Miao-Na Bai, Ji-Xiang Wang, Ji-Xiang Wang, Xiao-Wei Li, Xiao-Wei Li, Xiao-Wei Li, Jing-Xian Wang, Jing-Xian Wang, Yu-Hang Wang, Yu-Hang Wang, Yin Liu, Yin Liu, Yin Liu, Jing Gao, Jing Gao, Jing Gao, Jing Gao

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Interpretable model based on MRI radiomics to predict the expression of Ki-67 in breast cancer by Li Zhang, Qinglin Du, Mengyi Shen, Xin He, Dingyi Zhang, Xiaohua Huang

“…The Shapley Additive Explanation (SHAP) algorithm was employed to explain the optimal model, and the AUC was used to assess the model’s performance. …”
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