Develop and validate a machine learning model to predict the risk of persistent pain after percutaneous transforaminal endoscopic discectomy by Jun Yuan, Jun Fu

“…Each model was optimized through grid search and 10-fold cross-validation. …”
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Bond behavior of galvanized iron fiber reinforced concrete with recycled coarse aggregate and model prediction using machine learning by Md. Jahidul Islam, Ummul Wara Labiba, Tasfiah Faisal Chowdhury, Md. Shahjalal, Tanvir Mustafy, Tanvir Hassan Tusher

“…The study also employed regression and machine learning (ML) models to predict bond strength. …”
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Leveraging machine learning models to evaluate immune infiltration in the ovarian cancer microenvironment: a single-cell analysis approach by Jie Liu, Baoguo Xia, Bingxin Li, Hui Liang

“…The models were evaluated and optimized using cross-validation methods. …”
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Explainable machine learning model for predicting decline in platelet count after interventional closure in children with patent ductus arteriosus by Song-Yue Zhang, Yi-Dong Zhang, Hao Li, Qiao-Yu Wang, Qiao-Fang Ye, Xun-Min Wang, Tian-He Xia, Yue-E He, Xing Rong, Ting-Ting Wu, Rong-Zhou Wu

“…BackgroundThis study aimed to apply four machine learning algorithms to develop the optimal model to predict decline in platelet count (DPC) after interventional closure in children with patent ductus arteriosus (PDA).MethodsData from children with PDA who underwent successful transcatheter closure at the Second Affiliated Hospital of Wenzhou Medical University and Yuying Children's Hospital from January 2016, to December 2022, were collected. …”
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Machine learning–based survival models for predicting rehospitalization of older hip fracture patients: a retrospective cohort study by Juahn Oh, Minah Park, Yonghan Cha, Jae-Hyun Kim, Seung Hoon Kim

“…Abstract Purpose To evaluate machine learning–based survival model roles in predicting rehospitalization after hip fractures to improve reduce the burden on the healthcare system. …”
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Statistical modeling and application of machine learning for antibiotic degradation using UV/persulfate-peroxide based advanced oxidation process by Musfekur Rahman Dihan, Md. Ashraful Alam, Surya Akter, Md. Abdul Gafur, Md. Shahinoor Islam

“…Based on RMSE, R2, MAE, and MSE values, the optimized ANN and RF (LSBoost) outperformed the SVM and all of the MLR models to predict the % removal and pHfinal for both antibiotics. …”
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Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis by Huan-Zhong Su, Dao-Hui Yang, Long-Cheng Hong, Yu-Hui Wu, Kun Yu, Zuo-Bing Zhang, Xiao-Dong Zhang

“…We aimed to establish and test machine learning (ML) models for classification of stromal subtypes in SPA based on ultrasound histogram analysis. …”
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Evaluating microstructural and machine learning predictive models for friction drilling of sustainable snail shell reinforced aluminium matrix composites by Rajesh Jesudoss Hynes Navasingh, R. Sankaranarayanan, Priyanka Mishra, Angela Jennifa Sujana J, Jebasingh Jeremiah Rajesh, Jana Petru

“…Random Forest (RF), Multilayer Perceptron (MLP), Gaussian Process Regression (GPR) and Support Vector Machine (SVM) models were employed for the prediction of distinct output responses.…”
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Sarcopenia prediction model based on machine learning and SHAP values for community-based older adults with cardiovascular disease in China by Peil Yu, Xinxin Zhang, Guoxuan Sun, Ping Zeng, Ping Zeng, Ping Zeng, Chu Zheng, Chu Zheng, Chu Zheng, Ke Wang, Ke Wang, Ke Wang

“…Subsequently, we built four machine learning (ML) models to predict SP. After 100 iterations, we selected the best performing model for risk stratification by comparing model discrimination and calibration. …”
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Game Theoretic Approach to QoS Oriented Machine Learning Model Development Toward 5G Network Migration Planning by Arjun Ray, Manish Kr. Yadav, Babu R. Dawadi, Krishna R. Bhandari

“…Our research addresses the growing strain on legacy 4G networks caused by exponential growth in mobile data traffic and connected devices. By combining machine learning and game theoretic modeling, we offer telecom operators an approach to make informed decision regarding optimal migration strategies. …”
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Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer patients by Qian Deng, Shan Li, Yuxiang Zhang, Yuanyuan Jia, Yanhui Yang

“…We sought to develop machine learning (ML) models to predict metastasis and prognosis in MIBC patients. …”
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A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study by Kelly Voigt, Yingtao Sun, Ayush Patandin, Johanna Hendriks, Richard Hendrik Goossens, Cornelis Verhoef, Olga Husson, Dirk Grünhagen, Jiwon Jung

“…MethodsForum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source. Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. …”
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Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning by Ye Wang, Zhen Pan, Huajun Cai, Shoufeng Li, Ying Huang, Jinfu Zhuang, Xing Liu, Guoxian Guan

“…Univariate and multivariate Cox regression analyses identified prognostic factors, which were then used to develop risk assessment models with 9 machine learning algorithms. Model hyperparameters were optimized using random search and 10-fold cross-validation. …”
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Machine learning models for the prediction of preclinical coal workers’ pneumoconiosis: integrating CT radiomics and occupational health surveillance records by Yankun Ma, Fengtao Cui, Yulong Yao, Fuhai Shen, Hongyi Qin, Bing Li, Yan Wang

“…Finally, five machine learning models were implemented to predict the preclinical stage of CWP. …”
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Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis by Ibrahim Mohammadzadeh, Bardia Hajikarimloo, Behnaz Niroomand, Nasira Faizi, Pooya Eini, Mohammad Amin Habibi, Alireza Mohseni, Mohammadmahdi Sabahi, Abdulrahman Albakr, Michael Karsy, Hamid Borghei-Razavi

“…Abstract Background Predicting pituitary adenoma (PA) recurrence after surgical resection is critical for guiding clinical decision-making, and machine learning (ML) based models show great promise in improving the accuracy of these predictions. …”
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Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study by Ran Zhang, Tian Li, Fan Fan, Haoying He, Liuyi Lan, Dong Sun, Zhipeng Xu, Sisi Peng, Jing Cao, Juan Xu, Xiaoxiang Peng, Ming Lei, Hao Song, Junjian Zhang

“…This study aimed to develop and validate an interpretable machine learning (ML) model for VaDep to serve as a clinical support tool. …”
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Machine learning-driven model for predicting knowledge, attitudes, and practices regarding medication safety among residents in Hubei, China by Chao Mei, Chao Mei, San-Lan Wu, San-Lan Wu, Tao Zhou, Tao Zhou, Yong-Ning Lv, Yong-Ning Lv, Yu Zhang, Yu Zhang, Chen Shi, Chen Shi, Wei-Jing Gong, Wei-Jing Gong

“…The Ordered Multinomial Logistic Regression model was most accurate for practice prediction (training accuracy: 0.6471, Kappa: 0.3421; validation accuracy: 0.6302, Kappa: 0.3153). …”
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Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study. by Thomas Callender, Fergus Imrie, Bogdan Cebere, Nora Pashayan, Neal Navani, Mihaela van der Schaar, Sam M Janes

“…In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening.…”
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Predicting the molecular subtypes of 2021 WHO grade 4 glioma by a multiparametric MRI-based machine learning model by Wenji Xu, Yangyang Li, Jie Zhang, Zhiyi Zhang, Pengxin Shen, Xiaochun Wang, Guoqiang Yang, Jiangfeng Du, Hui Zhang, Yan Tan

“…Objectives To develop and validate a machine learning (ML) model using multiparametric MRI for the preoperative differentiation of astrocytoma, CNS WHO grade 4, and glioblastoma (GBM), isocitrate dehydrogenase-wild-type (IDH-wt) (WHO 2021) (Task 1:grade 4 vs. …”
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Machine learning-based risk prediction models for bronchopulmonary dysplasia in preterm infants: a high-altitude cohort study by Heng Zhang, Fei Wang, Hongying Mi, Xiaoyan Xu, Ou Jiang, Yilin Lin, Lianfang Tang, Ziwei Li, Rui Ba

“…Severe BPD was independently associated with prolonged invasive ventilation (>7 days) (OR 4.12, 95% CI 2.78 to 6.11), elevated C reactive protein (>10 mg/L) (OR 2.87, 95% CI 1.93 to 4.26) and patent ductus arteriosus (OR 2.53, 95% CI 1.71 to 3.74). Machine learning models demonstrated strong predictive performance; the optimal XGBoost model achieved an area under the curve of 0.89 (95% CI 0.85 to 0.93), an F1 score of 0.82, a Matthews Correlation Coefficient of 0.73 and a balanced accuracy of 0.85. …”
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