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All Journal Journal of Computer, Electronic, and Telecommunication (COMPLETE)
Trinidya, Fike
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods Mulyani, Sri Hasta; Wijaya, Nurhadi; Trinidya, Fike
Journal of Computer, Electronic, and Telecommunication (COMPLETE) Vol. 4 No. 2 (2023): December
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/complete.v4i2.394

Abstract

This study addresses the problem of heart disease detection, a critical concern in public health. The research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms in diagnosing heart disease using a dataset comprising 14 features. The primary objective is to determine whether CNNs can provide more accurate and reliable results than traditional techniques. The research employs rigorous preprocessing, normalizing relevant features, and splits the dataset into an 80-20 training-testing split. The model is trained for 300 epochs with a batch size of 64, and performance evaluation is conducted using confusion matrices and classification reports. The results reveal that the CNN model achieved a remarkable accuracy of 100%, demonstrating its potential to outperform conventional machine learning algorithms. These findings emphasize the significance of deep learning techniques in improving heart disease diagnostics, although further research is needed to optimize CNN models and address interpretability concerns for practical implementation in healthcare settings.
Co-Authors Sri Hasta Mulyani Wijaya, Nurhadi