Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model by Fuyu Guo, Shiwei Sun, Xiaoqian Deng, Yue Wang, Wei Yao, Peng Yue, Shaoduo Wu, Junrong Yan, Junrong Yan, Junrong Yan, Xiaojun Zhang, Xiaojun Zhang, Xiaojun Zhang, Yangang Zhang, Yangang Zhang, Yangang Zhang

“…Radiomics and deep learning (3D-Resnet18) features were extracted and fused. …”
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A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study by Sheng Cheng, Xian-Tao Zeng, Xia Liang, Zhi-Liang Hong, Jian-Chuan Yang, Zi-Ling You, Song-Song Wu

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Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics by Zhenze Zhang, Zhenze Zhang, Caiqing Zhao, Caiqing Zhao, Yijun Zhou, Ling Yao, Peng Liu, Ziling Fang, Nian Fang, Nian Fang

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Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning by Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari, Francesco Zaccardi, Navjot Kaur, Navjot Kaur, Danielle Fitt, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan

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Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches by Md. Atiqur Rahman, Israt Jahan, Maheen Islam, Taskeed Jabid, Md Sawkat Ali, Mohammad Rifat Ahmmad Rashid, Mohammad Manzurul Islam, Md. Hasanul Ferdaus, Md Mostofa Kamal Rasel, Mahmuda Rawnak Jahan, Shayla Sharmin, Tanzina Afroz Rimi, Atia Sanjida Talukder, Md. Mafiul Hasan Matin, M. Ameer Ali

“…This fact highlights the need for accurate machine learning algorithms (MLAs) for analyzing, monitoring, and diagnosing sleep disorders. …”
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Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture by Safa E. El-Mahroug, Ayman A. Suleiman, Mutaz M. Zoubi, Saif Al-Omari, Qusay Y. Abu-Afifeh, Heba F. Al-Jawaldeh, Yazan A. Alta’any, Tariq M. F. Al-Nawaiseh, Nisreen Obeidat, Shahed H. Alsoud, Areen M. Alshoshan, Fayha M. Al-Shibli, Rakad Ta’any

“…Yield projections under each scenario were further analyzed using machine learning algorithms—random forest and gradient boosting regression—to quantify the influence of individual climate variables. …”
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Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

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Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition by Yingzhe Yu, Huiqiong Jia, Li Zhang, Suling Xu, Xiaoxia Zhu, Jiucun Wang, Fangfang Wang, Lianyi Han, Haoqiang Jiang, Qiongyan Zhou, Chao Xin

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Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture by Tang M, Zhang M, Dang Y, Lei M, Zhang D

“…All patients were randomly splitting into a training group and a validation group according to the ratio of 7:3. The majority of patients were used to train models, which was tuned using a series of algorithms, including decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), neural network (NN), and logistic regression (LR).Results: The incidence of postoperative pneumonia was 7.2% (40/555). …”
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Evaluation of the shielding initiative in Wales (EVITE Immunity): protocol for a quasiexperimental study by Stephen Jolles, Ashley Akbari, Andrew Carson-Stevens, Helen Snooks, Alan Watkins, Adrian Edwards, Ann John, Alison Porter, Victoria Williams, Bridie Angela Evans, Ronan Lyons, Bernadette Sewell, Mark Rhys Kingston, Tony Whiffen, Jane Lyons, Rowena Bailey, Catherine A Thornton, Lesley Bethell, Samantha Bufton, Lucy Dixon

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An integrated machine learning and fractional calculus approach to predicting diabetes risk in women by David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez

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Enhancing Airport Traffic Flow: Intelligent System Based on VLC, Rerouting Techniques, and Adaptive Reward Learning by Manuela Vieira, Manuel Augusto Vieira, Gonçalo Galvão, Paula Louro, Alessandro Fantoni, Pedro Vieira, Mário Véstias

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Enhancing liver disease diagnosis with hybrid SMOTE-ENN balanced machine learning models—an empirical analysis of Indian patient liver disease datasets by Ritu Rani, Garima Jaiswal, Nancy, Lipika, Shashi Bhushan, Fasee Ullah, Prabhishek Singh, Manoj Diwakar, Manoj Diwakar

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Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer by Ruoya Wang, Shouliang Cai, Qing Gao, Yidong Chen, Xue Han, Fangjian Shang, Chunyan Liang, Guolian Zhu, Bo Chen

“…The predictive performance of the polyamine metabolism model was validated by ROC curve analysis (training cohort: METABRIC, AUC3years=0.684; validation cohort: GSE86166, AUC3years=0.682). …”
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Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation by Nan Zhang, Ke Lin, Bin Qiao, Liwei Yan, Dongdong Jin, Daopeng Yang, Yue Yang, Xiaohua Xie, Xiaoyan Xie, Bowen Zhuang

“…The PDP and SHAP algorithms were employed for visual interpretation. …”
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Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

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MRI radiomics model for predicting tumor immune microenvironment types and efficacy of anti-PD-1/PD-L1 therapy in hepatocellular carcinoma by Rui Zhang, Wei Peng, Yao Wang, Yunping Jiang, Junli Wang, Siying Zhang, Zhi Li, Yushu Shi, Feng Chen, Zhan Feng, Wenbo Xiao

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Organization of Expert Activity on Digital Network Platforms by V. V. Zotov

“…Currently, expert-analytical activity is being developed on digital network platforms as sociotechnical systems that organize algorithmized relationships between actors and actant in the field of assessing a problem situation and making decisions to resolve it. …”
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Improving T2D machine learning-based prediction accuracy with SNPs and younger age by Cynthia AL Hageh, Andreas Henschel, Hao Zhou, Jorge Zubelli, Moni Nader, Stephanie Chacar, Nantia Iakovidou, Haralampos Hatzikirou, Antoine Abchee, Siobhán O’Sullivan, Pierre A. Zalloua

“…Methods: Six models—Random Forest, Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, Gradient Boosting Machine, and Decision Tree—were trained and tested on a discovery dataset (N=3,546) and validated in the UK Biobank (N=31,620). …”
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Machine learning-based prognostic prediction for acute ischemic stroke using whole-brain and infarct multi-PLD ASL radiomics by Zhenyu Wang, Chaojun Jiang, Xianxian Zhang, Tianchi Mu, Qingqing Li, Shu Wang, Congsong Dong, Yuan Shen, Zhenyu Dai, Fei Chen

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