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 (ASSET)
Vol 6, No 1 (2024): November-January

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.