Abstract:
One of the major challenges faced by doctors is making decisions regarding disease related
to the patient’s condition. Our topic addresses one of the problems related to this, which is :
can data be further used to improve the accuracy of medical decision-making ? To solve this
problem, we propose a disease classification mechanism based on medical data and patientassociated
symptoms, relying on deep learning algorithms.
The proposed solution to the presented problem relies on building models for standard
deep artificial networks by applying different optimization algorithms (SGD, ADAM) and applying
PCA for dimensionality reduction. The results of comparing the performance of different
models showed high efficiency in applying dimensionality reduction with the SGD optimization
algorithm in terms of handling new data and the time required for training.
We conclude from the results the effectiveness of deep learning in solving problems associated
with classification and data exploitation.
