Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy by Linrui Li, Zhihui Qin, Juan Bo, Jiaru Hu, Yu Zhang, Liting Qian, Jiangning Dong

“…The optimal radiomics model was selected using receiver operating curve analysis. …”
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Development and Evaluation of a Machine Learning Model for Predicting 30-Day Readmission in General Internal Medicine by Abdullah M. Al Alawi, Mariya Al Abdali, Al Zahraa Ahmed Al Mezeini, Thuraiya Al Rawahia, Eid Al Amri, Maisam Al Salmani, Zubaida Al-Falahi, Adhari Al Zaabi, Amira Al Aamri, Hatem Al Farhan, Juhaina Salim Al Maqbali

“…This study aimed to develop and evaluate machine learning (ML) models for predicting 30-day readmissions in patients admitted under a GIM unit and to identify key predictors to guide targeted interventions. …”
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Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model by Qingqing Lin, Qingqing Lin, Wenxiang Zhao, Wenxiang Zhao, Hailin Zhang, Hailin Zhang, Wenhao Chen, Sheng Lian, Qinyun Ruan, Qinyun Ruan, Zhaoyang Qu, Zhaoyang Qu, Yimin Lin, Yimin Lin, Dajun Chai, Dajun Chai, Dajun Chai, Dajun Chai, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin

“…We aimed to use interpretable machine learning (ML) methods to develop a risk prediction model for HF in AMI patients.MethodsWe retrospectively included patients initially with AMI who received percutaneous coronary intervention (PCI) in our hospital from November 2016 to February 2020. …”
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Mortality prediction of heart transplantation using machine learning models: a systematic review and meta-analysis by Ida Mohammadi, Setayesh Farahani, Asal Karimi, Saina Jahanian, Shahryar Rajai Firouzabadi, Mohammadreza Alinejadfard, Alireza Fatemi, Bardia Hajikarimloo, Mohammadhosein Akhlaghpasand

“…IntroductionMachine learning (ML) models have been increasingly applied to predict post-heart transplantation (HT) mortality, aiming to improve decision-making and optimize outcomes. …”
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Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding by Yani Zhang, Qiankun Li, Haijun Duan, Liang Tan, Ying Cao, Junxin Chen

“…The extreme gradient boosting (XGBoost) machine learning method was employed to establish a prediction model for prolonged SARS-CoV-2 shedding and analyze significant risk factors. …”
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An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer by Ziqi Wu, Liya Gong, Jingwen Luo, Xiaobo Chen, Fan Yang, Junyan Wen, Yanyu Hao, Zhishan Wang, Ruozhen Gu, Yuqin Zhang, Hai Liao, Ge Wen

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Construction and Validation of a Machine Learning-Based Risk Prediction Model for Sleep Quality in Patients with OSA by Tong Y, Wen K, Li E, Ai F, Tang P, Wen H, Guo B

“…A LightGBM model was constructed and compared with other machine learning models, in terms of performance metrics such as the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). …”
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Construction of a Wilms tumor risk model based on machine learning and identification of cuproptosis-related clusters by Jingru Huang, Yong Li, Xiaotan Pan, Jixiu Wei, Qiongqian Xu, Yin Zheng, Peng Chen, Jiabo Chen

“…Finally, the WT risk prediction model was constructed by four machine learning methods: random forest, support vector machine (SVM), generalized linear and extreme gradient strength model. …”
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Machine learning-based predictive model for acute pancreatitis-associated lung injury: a retrospective analysis by Zhaohui Du, Qiaoling Ying, Yisen Yang, Huicong Ma, Hongchang Zhao, Jie Yang, Zhenjie Wang, Chuanming Zheng, Shurui Wang, Qiang Tang

“…These two models were selected as the optimal models for the development of an online calculator for clinical applications and risk stratification.ConclusionWe developed and internally validated a machine learning model to predict APALI, showing strong performance in our study population. …”
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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…Potential risk factors for postoperative AVN were screened using univariate and multivariate logistic regression analyses. Six machine learning algorithms were employed to construct the prediction models. …”
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Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning by Kennedy C. Onyelowe, Viroon Kamchoom, Shadi Hanandeh, S. Anandha Kumar, Rolando Fabián Zabala Vizuete, Rodney Orlando Santillán Murillo, Susana Monserrat Zurita Polo, Rolando Marcel Torres Castillo, Ahmed M. Ebid, Paul Awoyera, Krishna Prakash Arunachalam

“…Abstract Physics-informed modeling (PIM) using advanced machine learning (ML) represents a paradigm shift in the field of concrete technology, offering a potent blend of scientific rigor and computational efficiency. …”
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Multi-machine learning model based on radiomics features to predict prognosis of muscle-invasive bladder cancer by Bin Wang, Zijian Gong, Peide Su, Guanghao Zhen, Tao Zeng, Yinquan Ye

“…Additionally, 54 patients with muscle-invasive bladder cancer were retrospectively collected from our hospital to serve as an external test group; their enhanced CT imaging data were analyzed and processed to identify the most relevant radiomic features. Five distinct machine learning methods were employed to develop the optimal radiomics model, which was then combined with clinical data to create a nomogram model aimed at accurately predicting the overall survival (OS) of patients with muscle-invasive bladder cancer. …”
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Machine learning‐based model for worsening heart failure risk in Chinese chronic heart failure patients by Ziyi Sun, Zihan Wang, Zhangjun Yun, Xiaoning Sun, Jianguo Lin, Xiaoxiao Zhang, Qingqing Wang, Jinlong Duan, Li Huang, Lin Li, Kuiwu Yao

“…Abstract Aims This study aims to develop and validate an optimal model for predicting worsening heart failure (WHF). …”
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Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis by Jinxi Yang, Siyao Zeng, Shanpeng Cui, Junbo Zheng, Hongliang Wang

“…Early detection and accurate prediction of ARDS can significantly improve patient outcomes. While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of consensus on the most effective model or methodology. …”
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Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions by Sokea Teng, Jung-Yeon Kim, Seob Jeon, Hyo-Wook Gil, Jiwon Lyu, Euy Hyun Chung, Kwang Seock Kim, Yunyoung Nam

“…Statistical analyses of the results for the most effective classifier model demonstrate that the support vector machine (SVM) is more effective than other classifiers across all sensor locations, with statistically significant differences in performance. …”
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Assimilating Observed Surface Pressure Into ML Weather Prediction Models by L. C. Slivinski, J. S. Whitaker, S. Frolov, T. A. Smith, N. Agarwal

“…Abstract There has been a recent surge in development of accurate machine learning (ML) weather prediction models, but evaluation of these models has mainly been focused on medium‐range forecasts, not their performance in cycling data assimilation (DA) systems. …”
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Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Induced by Microwave Radiation and Their Associated Strength Reduction Patterns: Employing Meta-Heuristic Opti... by Zhongyuan Gu, Xin Xiong, Chengye Yang, Miaocong Cao

“…Utilizing the Pied Kingfisher Optimizer (PKO) alongside Extreme Gradient Boosting (XGBoost), we developed a PKO-XGBoost machine learning model to elucidate the relationship between UCSA and the nine additional parameters. …”
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Mathematical and Machine Learning Innovations for Power Systems: Predicting Transformer Oil Temperature with Beluga Whale Optimization-Based Hybrid Neural Networks by Jingrui Liu, Zhiwen Hou, Bowei Liu, Xinhui Zhou

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Long Term Optimal DG Placement Considering Transmission System Reliability and Load Uncertainty by Ali Badri, Mahdy Norouzi

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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine by S. Syama, J. Ramprabhakar, R Anand, V. P. Meena, Josep M. Guerrero

“…Further, permutation entropy is employed to extract the complexity of IMFs for filtering and reconstruction of decomposed components to alleviate the difficulty of direct modeling. Then, a unique swarm intelligence technique, the non-linear dimension learning Hunting Whale Optimization Algorithm (NDLHWOA), is devised to optimize regularized extreme learning machine model parameters to capture the implicit information of each reconstructed sub-series. …”
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