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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Advance Sustainable Science, Engineering and Technology (ASSET)
Cahyani, Ailsa Nurina
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology

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Journal : Advance Sustainable Science, Engineering and Technology (ASSET)

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Heart Disease Classification Using Deep Neural Network with SMOTE Technique for Balancing Data Cahyani, Ailsa Nurina; Zeniarja, Junta; Winarno, Sri; Putri, Rusyda Tsaniya Eka; Maulani, Ahmad Alaik
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17521

Abstract

Heart disease is the leading cause of premature death worldwide. According to the WHO, heart disease causes about 30% of the total 58 million deaths and mostly occurs in individuals who are in their productive age. This condition can occur to anyone, including individuals who do not show symptoms of heart disease. However, heart disease can be prevented with early detection. By understanding the various risk factors that can increase the potential for heart disease. Therefore, this study aims to classify heart disease using Deep Neural Network algorithm and SMOTE technique to overcome data imbalance. This research resulted in a validation accuracy of 90% with precision evaluation of 0.85, recall 0.92, and f1-score 0.88. Based on the results obtained, the Deep Neural Network algorithm after SMOTE is superior to the model without SMOTE.
Co-Authors Junta Zeniarja Maulani, Ahmad Alaik Putri, Rusyda Tsaniya Eka Sri Winarno