№ files_lp_4_process_1_40578
File format: docx Character count: 35121 File size: 153 KB
Year: 2020
Region / City: International
Topic: Machine Learning
Document Type: Research Article
Organization / Institution: International Journal of Machine Learning and Cybernetics
Authors: Mukherjee, H., Obaidullah, S.M., Santosh, K.C., Bakhshandeh, S., Azmi, R., Teshnehlab, M., Gu, X., Chung, Fl., Wang, S., Zhou, W., Qiao, S., Yi, Y., Song, Z., Cui, K., Cheng, G., Qin, K., Lin, H., Jiang, Y., Wang, L., Gao, C., Zhao, N., Zeng, XQ., Chen, SF., Xiang, R., Zhang, TL., Chen, R., Yang, X., Zhong, Sh., Huang, X., Xiao, Z., Hu, X., Sun, B., Chen, X., Zhang, Y., Huang, D., Lin, H., Chen, H., Zhang, Y., Li, G., Luo, X., Li, Y., Wang, W., Zhang, CX., Kim, SW., Zhang, JS.
Target Audience: Researchers, Data Scientists, Academics
Period of Validity: 2020
Approval Date: January 2020
Date of Changes: None
Price: 8 / 10 USD
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Taylor Series – An Approximation for the Lazy Physicist
Year: 1715
Region / City: Not specified
Subject: Mathematics, Calculus, Polynomial Approximations
Document Type: Essay
Institution: Not specified
Author: Not specified
Target Audience: Physicists, Mathematicians, Students of Mathematics
Period of Application: Not specified
Approval Date: Not specified
Date of Changes: Not specified
Note: Contextual Description
Machine Learning-Based Cardiovascular Disease Detection Using Optimal Feature Selection
Year: 2026
Region / City: Not specified
Topic: Cardiovascular disease diagnosis
Document type: Research study
Organization / Institution: Not specified
Author: Not specified
Target audience: Healthcare professionals, data scientists
Methods: Machine learning algorithms, feature selection
Algorithms: Random Forest Classifier, Support Vector Machines (SVM), XGBoost
Software: Python, Visual Studio Code
Objective: Improve diagnostic accuracy and efficiency for cardiovascular diseases
Outcome: High accuracy and precision in disease detection, reduced false positives
Data: Patient medical history, clinical measurements, lifestyle factors
Machine learning-based decision tool for selecting patients with idiopathic acute pancreatitis for endosonography to exclude a biliary aetiology
Year: 2023
Region / City: Munich, Germany; Gliwice, Poland; Göttingen, Germany
Subject: Acute pancreatitis, Biliary pancreatitis, Microlithiasis, Sludge, Machine learning
Document type: Research article
Organization / Institution: LMU University Hospital, Maria Sklodowska-Curie National Research Institute of Oncology, University Medical Center Göttingen
Author: Simon Sirtl, Michal Żorniak, Eric Hohmann, Georg Beyer, Miriam Dibos, Annika Wandel, Veit Phillip, Christoph Ammer-Herrmenau, Albrecht Neesse, Christian Schulz, Jörg Schirra, Julia Mayerle, Ujjwal Mukund Mahajan
Target audience: Medical researchers, healthcare professionals, gastroenterologists
Period of validity: 2015–2020
Approval date: August 28, 2023
Date of changes: N/A
Corresponding author: Julia Mayerle
Supported by: Deutsche Forschungsgemeinschaft (German Research Foundation), United European Gastroenterology Research Fellowship
Keywords: Machine learning, Endosonography, Acute pancreatitis, Microlithiasis, Sludge
DOI: https://dx.doi.org/10.3748/wjg.v29.i35.5138
Context description: A retrospective study on the development of a machine learning-based model to predict biliary sludge and microlithiasis in patients with idiopathic acute pancreatitis, supporting decision-making for endosonography.
Topics: MFCC-2 Lazy Learning-Based