KLASIFIKASI PENYAKIT JANTUNG BERBASIS CONVOLUTIONAL NEURAL NETWORK MENGGUNAKAN BASIS DATA THE PHYSIKALISCH-TECHNISCHE BUNDESANSTALT-XL (PTB-XL)

KHAIRUNNISA, CHOLIDAH ZUHROH and Nurmaini, Siti (2023) KLASIFIKASI PENYAKIT JANTUNG BERBASIS CONVOLUTIONAL NEURAL NETWORK MENGGUNAKAN BASIS DATA THE PHYSIKALISCH-TECHNISCHE BUNDESANSTALT-XL (PTB-XL). Undergraduate thesis, Sriwijaya University.

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Abstract

Heart disease is one of the leading causes of death worldwide and can be identified based on the patterns of electrical activity in the heart using an Electrocardiogram (ECG). The importance of early detection and classification of heart disease has led to the use of innovative methods, such as Convolutional Neural Networks (CNN). The PTB-XL ECG data has been processed and prepared to train and test the CNN model. This deep learning approach aims to recognize characteristic patterns in ECG signals that indicate specific types of heart disease.In this research, the CNN network structure was optimized and designe, and then trained using the PTB-XL ECG data. The research was divided into several models, and the best model was obtained. The experimental results showed that the best model achieved an accuracy of 86.86%, sensitivity of 75.28%, specificity of 75.28%, precision of 83.56%, F1 Score of 75.25%, and an error of 13.14%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Elektrokardiogram (EKG), Klasifikasi, PTB-XL, Myocardial Infarction, Deep Learning, Convolutional Neural Network.
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Cholidah Zuhroh Khairunnisa
Date Deposited:	08 Aug 2023 02:44
Last Modified:	08 Aug 2023 02:44
URI:	http://repository.unsri.ac.id/id/eprint/126223

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