Klasifikasi Coronary Heart Disease (CHD) Berbasis Optimasi DNN dan Inisialisasi Kaiming He

 (*)Lia Andiani Mail (Universitas Sriwijaya, Palembang, Indonesia)
 Sukemi Sukemi (Universitas Sriwijaya, Palembang, Indonesia)
 Dian Palupi (Universitas Sriwijaya, Palembang, Indonesia)
 Nurul Afifah (Universitas Sriwijaya, Palembang, Indonesia)

(*) Corresponding Author

Submitted: October 29, 2020; Published: January 22, 2021

DOI: http://dx.doi.org/10.30865/mib.v5i1.2559


CHD is chest pain or discomfort that occurs if the area of the heart muscle does not get enough oxygen-rich blood. CHD is also known as coronary artery disease. CHD is increasing every year with a significant number of deaths. A learning algorithm is proposed to get better performance in accuracy, sensitivity, and specificity in CHD interpretation. Accuracy can be improved by adding a Kaiming He (2015) weight initialization optimization technique to the DNN structure. Therefore we propose that DNN is optimized with a Kaiming He weight initialization technique so that it can overcome weaknesses in the data variant. This is evidenced by the results of the accuracy performance of 98.73%. Initialization of kaiming he weights is proven to improve accuracy and overcome the problem of large data variants between classes


CHD; Kaiming He; DNN; Accuracy; Sensitivity; Specificity

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