Classification of Normal /Abnormal Heart Sound Recordings

dc.contributor.author	AOUN Fatima, ZERIFI Razika
dc.date.accessioned	2024-09-20T20:59:33Z
dc.date.available	2024-09-20T20:59:33Z
dc.date.issued	2024-06-09
dc.identifier.uri	http//localhost:8080/jspui/handle/123456789/11914
dc.description.abstract	In this project, we address the medical field related to the cardiovascular system and heart diseases. Our objective is to develop a solution based on artificial intelligence techniques, in particular those of Machine Learning, in the form of an intelligent diagnostic support system to detect heartbeat anomalies using the dataset of the 2016 PhysioNet/CinC Challenge. For this we apply techniques for extracting temporal characteristics from PCG signals. In order to train the model to recognize heart sounds, we used various algorithms such as Random Forest, KNN and SVM. The results obtained are very satisfactory with an accuracy score of 92% and demonstrate the effectiveness and reliability of our intelligent system, which aims to be a diagnostic aid tool for health practitioners.	en_US
dc.language.iso	en	en_US
dc.publisher	Université de Echahid Cheikh Larbi Tébessi –Tébessa-	en_US
dc.subject	CAD, AI, Machine Learning, SVM, Random Forest, KNN, PCG; heartbeat	en_US
dc.title	Classification of Normal /Abnormal Heart Sound Recordings	en_US
dc.type	Thesis	en_US