Data Science

The present study aims to use machine learning algorithms to predict diagnoses of stress, anxiety and depression as the most common psychiatric disorders, using the dataset collected as part of this work. The dataset consisted of approximately 700 records using an online survey, which was based on the Depression, Anxiety and Stress International Scale (DASS21). The data was collected from Palestinian community participants and university students. To ensure the effectiveness of applying artificial intelligence algorithms, the data was processed before analyzing it.

In recent years, deep learning models have provided various applications in various fields, especially in the medical fields such as neuroscience. Thus, ensuring the interpretation of the results predicted by deep learning models has been an important challenge, especially exploring the hidden layers in these models, and this is called black box interpretation.

This thesis presents an integrated two-step learning framework that uses deep learning to simplify and increase the accuracy of the breast cancer diagnosis process. This innovative methodology differs from traditional methods by using raw, unmarked X-ray images, and this would facilitate the pre-treatment phase.