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La classification des Maladies via une Analyse Médicale Basée sur l’Apprentissage Profond

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dc.contributor.author BENSADI, Houssem
dc.contributor.author Eddine BENSEGHIR, Aya
dc.date.accessioned 2024-09-18T11:02:50Z
dc.date.available 2024-09-18T11:02:50Z
dc.date.issued 2024
dc.identifier.issn MM/817
dc.identifier.uri https://dspace.univ-bba.dz:443/xmlui/handle/123456789/5391
dc.description.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. en_US
dc.language.iso fr en_US
dc.publisher UNIVERSITY BBA en_US
dc.subject Artificial intelligence, Deep learning, Classification, diseases, Optimisation, Symptoms. Ã en_US
dc.subject Intelligence artificielle , Apprentissage profond ,Classification, Optimisation, Maladies, Symptômes. v en_US
dc.title La classification des Maladies via une Analyse Médicale Basée sur l’Apprentissage Profond en_US
dc.type Thesis en_US


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