Using machine learning models to predict post-revascularization thrombosis in PAD by Samir Ghandour, Adriana A. Rodriguez Alvarez, Isabella F. Cieri, Shiv Patel, Mounika Boya, Rahul Chaudhary, Rahul Chaudhary, Rahul Chaudhary, Anna Poucey, Anahita Dua

“…Multiple MLMs, including logistic regression, XGBoost, and decision tree algorithms, were developed and evaluated using a 70–30 train-test split and five-fold cross-validation. …”
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A model of indoor thermal condition based on traditional acehnese houses using artificial neural network by Muslimsyah, Abdul Munir, Yuwaldi Away, Abdullah, Teuku Yuliar Arif, Andri Novandri

“…The sample data used to train the ANN model consists of thermal data from five different rooms and meteorological data. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…First, two-sample mendelian randomization (MR) assessed causality between five domains (metabolic profiles, body composition, cardiovascular markers, behavioral traits, and psychological states) and the risk of migraine. Second, we trained ensemble machine learning (ML) algorithms that incorporated these factors, with Shapley Additive exPlanations (SHAP) value analysis quantifying predictor importance. …”
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Advanced Deep Learning Fusion Model for Early Multi-Classification of Lung and Colon Cancer Using Histopathological Images by A. A. Abd El-Aziz, Mahmood A. Mahmood, Sameh Abd El-Ghany

“…The proposed DL model was evaluated using the LC25000 dataset, which contains colon and lung HIs. …”
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The State of Artificial Intelligence and its Prospects in Pakistan's Medical Sector by Rohail Akhtar Habib, Yumna Sattar Khan

“…Predictive analytics using machine learning algorithms has become more popular in Pakistani healthcare. …”
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EEG microstate analysis in children with prolonged disorders of consciousness by Yi Zhang, Zhichong Hui, Yuwei Su, Weihang Qi, Guangyu Zhang, Liang Zhou, Jiamei Zhang, Kaili Shi, Yonghui Yang, Lei Yang, Gongxun Chen, Sansong Li, Mingmei Wang, Dengna Zhu

“…Support vector machine (SVM) models were trained using combined temporal and spatial microstate features, optimized via grid search and random search algorithms. …”
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Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery by Man Xu, Hao Liu, Anran Dai, Qilian Tan, Xinlong Zhang, Rui Ding, Chen Chen, Jianjun Zou, Yongjun Li, Yanna Si

“…Feature selection was conducted via the Least Absolute Shrinkage and Selection Operator (LASSO) and Boruta algorithms. Five machine learning models, including logistic regression, multilayer perceptron, extreme gradient boosting, categorical boosting, and deep neural network (DNN), were trained using preoperative and intraoperative variables. …”
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Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database by De Su, Jie Zheng, Yue-kai Shao, Jun-ya Liu, Xin-xin Liu, Kun Yu, Bang-hai Feng, Hong Mei, Song Qin

“…LASSO regression was employed for feature selection, and various machine learning algorithms were utilized to train models, including logistic regression (LR), random forest (RF), and gradient boosting (GB), among others. …”
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A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema by Wenrui Lu, Kunhong Xiao, Xuemei Zhang, Yuqing Wang, Wenbin Chen, Xierong Wang, Yunxi Ye, Yan Lou, Li Li

“…Five machine learning algorithms—logistic regression, decision tree, multilayer perceptron, random forest, and support vector machine—were trained and validated. …”
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Predictive analysis of clinical features for HPV status in oropharynx squamous cell carcinoma: A machine learning approach with explainability by Emily Diaz Badilla, Ignasi Cos, Claudio Sampieri, Berta Alegre, Isabel Vilaseca, Simone Balocco, Petia Radeva

“…Materials and Methods:: We employed the RADCURE dataset clinical information to train six Machine Learning algorithms, evaluating them via cross-validation for grid search hyper-parameter tuning and feature selection as well as a final performance measurement on a 20% sample test set. …”
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Enhanced deep learning model for apple detection, localization, and counting in complex orchards for robotic arm-based harvesting by Tantan Jin, Xiongzhe Han, Pingan Wang, Zhao Zhang, Jie Guo, Fan Ding

“…The enhanced model was trained and evaluated on a comprehensive dataset, achieving precision, recall, F1 score, and mAP50 values of 81.43 %, 68.48 %, 74.40 %, and 81.68 %, respectively. …”
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A Step-by-Step Harmonization Process for Nutritional Epidemiology Purposes: A Methodological Work of the Collaborative PROMED-COG Pooled Cohorts Study by Federica Prinelli, Silvia Conti, Claire T. McEvoy, Caterina Trevisan, Stefania Maggi, Giuseppe Sergi, Marianna Noale

“…Here, we described our step-by-step dietary data harmonization process applied within the PROMED-COG pooled cohorts study aiming to evaluate the effect of nutrition on neurocognitive ageing. …”
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Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data by T. Radke, S. Fuchs, C. Wilms, I. Polkova, I. Polkova, I. Polkova, M. Rautenhaus, M. Rautenhaus

“…We also demonstrate application of the approach for finding the most relevant input variables (TMQ is found to be most relevant, while surface pressure is rather irrelevant) and evaluating detection robustness when changing the input domain (a CNN trained on global data can also be used for a regional domain, but only partially contained features will likely not be detected). …”
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Unveiling psychobiological correlates in primary Sjögren’s syndrome: a machine learning approach to determinants of disease burden by László V. Módis, László V. Módis, András Matuz, András Matuz, Zsófia Aradi, Ildikó Fanny Horváth, Antónia Szántó, Antal Bugán

“…Three machine learning algorithms were trained to predict outcome variables, first by each measure category, then on the entire set of predictor variables. …”
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Large Language Models and Artificial Neural Networks for Assessing 1-Year Mortality in Patients With Myocardial Infarction: Analysis From the Medical Information Mart for Intensive... by Boqun Shi, Liangguo Chen, Shuo Pang, Yue Wang, Shen Wang, Fadong Li, Wenxin Zhao, Pengrong Guo, Leli Zhang, Chu Fan, Yi Zou, Xiaofan Wu

“…An artificial neural network (ANN) algorithm derived from the SWEDEHEART (Swedish Web System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies) registry, termed SWEDEHEART-AI, can predict patient prognosis following acute myocardial infarction (AMI). …”
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Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnex... by Lu Liu, Wenjun Cai, Feibo Zheng, Hongyan Tian, Yanping Li, Ting Wang, Xiaonan Chen, Wenjing Zhu

“…Deep learning segmentation models were trained and selected to automatically segment adnexal masses. …”
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Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study by Jin-Hyun Park, Inyong Jeong, Gang-Jee Ko, Seogsong Jeong, Hwamin Lee

“…Five machine learning algorithms were compared, and the best-performing model was selected based on the area under the receiver operating characteristic curve. …”
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High-resolution surface soil moisture retrieval: A hybrid machine learning framework integrating change detection and downscaling for precision water management by Zihao Wang, Qi Gao, Michele Crosetto, Maria Jose Escorihuela

“…Among the evaluated algorithms, XGBoost model performed best, achieving an R2 of 0.933 and RMSE of 0.023 cm3/cm3. …”
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Oxidative balance score predicts chronic kidney disease risk in overweight adults: a NHANES-based machine learning study by Leying Zhao, Leying Zhao, Cong Zhao, Cong Zhao, Yuchen Fu, Yuchen Fu, Xiaochang Wu, Xiaochang Wu, Xuezhe Wang, Xuezhe Wang, Yaoxian Wang, Yaoxian Wang, Yaoxian Wang, Huijuan Zheng

“…Restricted cubic spline regression examined dose–response patterns, and subgroup analyses evaluated effect modifiers. Additionally, 14 machine learning algorithms were trained and validated using SMOTE-balanced data and five-fold cross-validation. …”
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Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study by Zhen Lu, Binhua Dong, Hongning Cai, Tian Tian, Junfeng Wang, Leiwen Fu, Bingyi Wang, Weijie Zhang, Shaomei Lin, Xunyuan Tuo, Juntao Wang, Tianjie Yang, Xinxin Huang, Zheng Zheng, Huifeng Xue, Shuxia Xu, Siyang Liu, Pengming Sun, Huachun Zou

“…We extracted CCP-specific risks of cervical intraepithelial neoplasia (CIN) and cervical cancer through weighted logistic regression analyses providing odds ratio (OR) estimates and 95% CIs. We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. …”
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