Publications by authors named "Shaymaa Sorour"
Article Synopsis
- Big data analytics is revolutionizing clinical decision-making by utilizing vast healthcare data to create evidence-based solutions, but it raises significant privacy and security concerns, especially regarding sensitive medical images stored in the cloud.
- The proposed project aims to enhance patient care through a machine learning-based anomaly detection system for medical images, addressing current limitations in existing detection methods that struggle with resource consumption and real-time performance.
- The research focuses on improving data processing techniques, including feature selection and handling missing values, employing advanced algorithms like recursive feature elimination and dynamic principal component analysis for more effective anomaly detection.
This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several algorithms are utilized such as k-nearest neighbor, Random Forest (RF), C4.
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