نوع المشرف

مشرف رئيسي

تاريخ الاشراف على الرسالة من

2019

الى

2020

اسم الطالب

مريم محمد جبار

ملخص الرسالة

The World Health Organization classified diabetes as a major global health problem and a leading cause of death worldwide. Studies have recently shown an increase in the epidemiology of diabetes owing to the increase in the related complications to diabetes, such as cardiac attacks and deaths. The prediction of medical datasets at the early stage is usually done using machine learning techniques (MLT) in a bid to save a life. Different data repositories host different medical datasets, which can be accessed for real-world applications. Machine learning (ML) can provide the necessary techniques as its primary mission is disease prediction from datasets. This thesis presents a machine learning model in which Artificial Neural Network (ANNs) trained using enhanced grey wolf optimizer (EGWO), for the classification of diabetes on publicly available “Pima Indian Diabetes (PID) dataset”. Several experiments have been executed on this dataset with variation in size of the population, techniques to handle missing data and their impact on classification accuracy have been discussed. Finally, the results are compared with other nature-inspired algorithms trained ANN. EGWO attained better results in terms of classification accuracy than the other algorithms. In addition, it was better than the original version of GWO. The convergence curve proved that EGWO had balanced the local and global search abilities because it was faster to reach better positions than the original GWO. In conclusion, the proposed algorithm trained ANN and enhanced classification accuracy. It can be used for predicting different medical case studies or different machine learning tasks.