How machine learning on real world clinical data improves adverse event recording for endoscopy by Stefan Wittlinger, Isabella C. Wiest, Mahboubeh Jannesari Ladani, Jakob Nikolas Kather, Matthias P. Ebert, Fabian Siegel, Sebastian Belle

“…This study evaluates a machine learning-based approach for systematically detecting endoscopic adverse events from real-world clinical metadata, including structured hospital data such as ICD-codes and procedure timings. …”
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Cropland Suitability Prediction Method Based on Biophysical Variables from Copernicus Data and Machine Learning by Dorijan Radočaj, Mateo Gašparović, Mladen Jurišić

“…The goal of this study was to propose and validate a method for predicting cropland suitability based on biophysical variables and machine learning according to an FAO land suitability standard using soybean (<i>Glycine max</i> L.) as a representative crop, aiming to provide an alternative to geographic information system (GIS)-based multicriteria analysis. …”
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Lost circulation intensity characterization in drilling operations: Leveraging machine learning and well log data by Ahmad Azadivash

“…After rigorous exploratory analysis and preprocessing of the data, seven machine learning methods are applied: Random Forest, Extra Trees, Decision Tree, XGBoost, k-Nearest Neighbors, Support Vector Machine, and Hard Voting. …”
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Using Machine Learning and Nationwide Population-Based Data to Unravel Predictors of Treated Depression in Farmers by Pascal Petit, Vincent Bonneterre, Nicolas Vuillerme

“…To complement these traditional studies, big data and machine learning (ML) can advantageously be harnessed. …”
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Machine learning prediction of coal workers’ pneumoconiosis classification based on few-shot clinical data by Jiaqi Jia, Jingying Huang, Yuming Cui, Dekun Zhang, Haiquan Li, Songquan Wang, Wenlu Hang

“…Objective Aiming at the problems of the long incubation period, insufficient early diagnosis, and lack of treatment methods of coal workers’ pneumoconiosis (CWP), the objective of this study is to accurately predict the CWP staging based on machine learning (ML) methods and small-sample clinical data. …”
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Multiclass classification of thalassemia types using complete blood count and HPLC data with machine learning by Muhammad Umar Nasir, Muhammad Zubair, Muhammad Tahir Naseem, Tariq Shahzad, Ahmed Saeed, Khan Muhammad Adnan, Amir H. Gandomi

“…This work aims to assess the performance of numerous combinations of machine learning methods to detect alpha and beta-thalassemia in their minor and major types. …”
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Monitoring water quality parameters using multi-source data-driven machine learning models by Yubo Zhao, Mo Chen, Jinyu He, Yanping Ma

“…Remote sensing technology, as an effective monitoring tool, provided real-time water quality data. Currently, most research primarily relied on reflectance analysis of remote sensing data, often overlooking the impact of environmental factors on aquatic environments. …”
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Predictions of Spartina alterniflora leaf functional traits based on hyperspectral data and machine learning models by Wei Li, Xueyan Zuo, Zhijun Liu, Leichao Nie, Huazhe Li, Junjie Wang, Zhiguo Dou, Yang Cai, Xiajie Zhai, Lijuan Cui

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Mapping Gridded GDP Distribution of China Based on Remote Sensing Data and Machine Learning Methods by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Futao Wang, Zhenqing Wang

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Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data by Swarnima Kollampallath Radhakrishnan, Dipanwita Nath, Dominic Russ, Dominic Russ, Dominic Russ, Laura Bravo Merodio, Laura Bravo Merodio, Laura Bravo Merodio, Priyani Lad, Folakemi Kola Daisi, Animesh Acharjee, Animesh Acharjee, Animesh Acharjee

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Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis by Xiaobing Li, Xiaobing Li, Xiaobing Li, Xiaobing Li, Xiaobing Li, Qian Li, Qian Li, Qian Li, Qian Li, Xinyi Xie, Wei Wang, Xuemei Li, Tingqiang Zhang, Tingqiang Zhang, Tingqiang Zhang, Tingqiang Zhang, Li Zhang, Li Zhang, Li Zhang, Li Zhang, Yongsheng Liu, Yongsheng Liu, Yongsheng Liu, Yongsheng Liu, Li Wang, Li Wang, Li Wang, Li Wang, Wutao Xie

“…ObjectiveThis study aims to develop a machine learning (ML) model that integrates computed tomography (CT) radiomics with clinical features to predict the progression of pulmonary interstitial fibrosis in patients with coalworker pneumoconiosis (CWP).MethodsClinical and imaging data from 297 patients diagnosed with CWP at The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College between December 2021 and December 2023 were analyzed. …”
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Fusing Machine Learning and AI to Create a Framework for Employee Well-Being in the Era of Industry 5.0 by Cosmina-Mihaela Rosca, Adrian Stancu

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A Robust Multivariate Time Series Classification Approach Based on Topological Data Analysis for Channel Fault Tolerance by Seong-Yeon Jeung, Jang-Woo Kwon

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Machine learning analysis of breast cancer treatment protocols and cycle counts: A case study at Mohammed vi hospital, Morocco by Houda AIT BRAHIM, Salah EL-HADAJ, Abdelmoutalib METRANE

“…This paper presents a new study of predicting patients' breast cancer treatment protocol and the corresponding treatment cycle based on machine learning algorithms. The data used were collected at Mohammed VI Hospital in Morocco, and it contains patient information with two targets (protocol and treatment cycle).After preparing the data and testing several machine learning algorithms, two models were developed: The first one, based on Gradient Boosting Classifier algorithm, successfully classified patient treatment protocols with an overall accuracy of 64 % across all categories and an impressive 94 % accuracy for the mode category, widely adopted in the hospital. …”
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Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer by Junyi Li, Shixin Li, Dongpo Zhang, Yibing Zhu, Yue Wang, Xiaoxiao Xing, Juefei Mo, Yong Zhang, Daixiang Liao, Jun Li

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Analyzing the Impact of Organic Food Consumption on Citizens Health Using Unsupervised Machine Learning by Giulio Angiolini, Giovanna Maria Dimitri

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Risks in Work-Integrated Learning: A Data-Driven Analysis by Xiao Xu

“…This study employs advanced data-driven and machine learning techniques to critically assess the integration of Work-Integrated Learning (WIL) into academic programs, with a focus on psychological well-being, financial, and equity and inclusion risks. …”
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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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Data-Driven Learning Models for Internet of Things Security: Emerging Trends, Applications, Challenges and Future Directions by Oyeniyi Akeem Alimi

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Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data by Erli Wu, Xuan Yin, Feng Liang, Xianqing Zhou, Jiamin Hu, Wanting Yuan, Feihan Gu, Jingxin Zhao, Ziyang Gao, Ming Cheng, Shouxiang Yang, Lei Zhang, Qingqing Wang, Qingqing Wang, Xiaoyu Sun, Xiaoyu Sun, Wei Shao, Wei Shao

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