5693
Shimaa Mohamed Abd El-Salam
Liver Diseases Diagnosis Using Machine
Learning Techniques
Machine Learning Approaches,Esophageal Varices, Features Selection, Statistical Analysis, Data Sciences,Performance Evaluation,Biomedical Informatics.
Artificial Intelligence (AI) involved in solving various medical problems. The clinical decision support systems focus on using AI to accomplish clinical advice for patient care based on the clinical data. In recent years, machine learning (ML) techniques have been used for early prediction of the diseases especially in the clinical decisions support systems. Esophageal Varices (EV) is recognized as a major healthcare problem worldwide, it is one of the most common side-effects of liver diseases. The standard method for diagnosing EV by the upper endoscopy is invasive, costly, and has many drawbacks. Screening all patients implies many endoscopies will be needed, which increases the workload of endoscopy units. This thesis aims to use the clinical information to build a predictive diagnosing model that uses a minimum number of the most significant parameters using ML techniques, trying to avoid unneeded endoscopy procedures. This study attempts to propose a quicker and more efficient techniques for disease diagnosis, leading to timely patient treatment.
2021
Ph.d
Al-Azhar
Engineering