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Yulisda, Desvina
Universitas Malikussaleh
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Journal : Bulletin of Computer Science Research

 1 
Analisis Performa Variational Quantum Classifier (VQC) dengan ZZ Feature Map dan Angle Encoding Untuk Mengidentifikasi Serangan Jantung Rahma, Hilmia; Dahlan Abdullah; Desvina Yulisda
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.712

Abstract

Heart attact is one of the leading causes of death worldwide, with mortality increasing due to delayed diagnosis and limited facilities in some regions. Early detection during the crucial first hour after symptoms appear (the golden hour) is crucial for reducing mortality and improving patient prognosis. This study aims to evaluate the performance of the Quantum Neural Network (QNN) by implementing the Variational Quantum Classifier (VQC) model using two types of feature maps: ZZ Feature Map and Angle Encoding, for heart attack detection classification using datasets from Kaggle. The research process includes dataset collection, Exploratory Data Analysis (EDA), data preprocessing and splitting, model building using ZZ Feature Map and Angle Encoding, ending with model performance evaluation. The results showed that VQC using ZZ Feature Map achieved an accuracy of 52.27% with a confusion matrix showing suboptimal predictions and relatively low precision, recall, and F1-score values. Meanwhile, VQC using Angle Encoding achieves an accuracy of 68.18% with a confusion matrix that shows a higher number of correct predictions and better precision, recall, and F1-score results compared to VQC using ZZ Feature Map. Evaluation using a confusion matrix shows that the model with Angle Encoding can minimize the prediction error. However, the accuracy achieved is not yet optimal for direct clinical implementation. These findings underscore the need for further development to enhance model performance, while serving as a promising first step toward establishing a foundation for developing more optimal QNN methods in the future.
Co-Authors -, Badriana -, Bakhtiar Agam Muarif Agus Adi Nursalim Ahmad Fikri Ahmad Fikri Ahmad Fikri Ahmad Nayan Akbar, Anggi Rizki Al Kautsar Aidilof, Hafizh Alfarizi, Reza Amalia, Nabila Ananda Faridhatul Ulva Andik Bintoro Angga Pratama Angga Pratama Apriani Riski Ar Razi Ar Razi Ashari, Muhammad Rayhan Aulia, Rika Baidhawi Baidhawi Balqis Yafis Chairil Anwar Cut Aura Putri K.D Dahlan Abdullah Dedi Fariadi Dini Rizki Ebi Putra Eka Susanti Eka Susanti Emi Maulani Eri Saputra Fadhliani, Fadhliani Fajar, Mutiara Febriandirza, Arafat Habib Muharry Yusdartono Hanif Hanif Himmatur Rijal Hisyam Maulana Hadi Hussain, Azham Ida Ayu Putu Sri Widnyani Iswadi Iswadi Kamila, Raisya Khairul Anshar Khairul Anshar Kurniawati Kurniawati Kurniawati Laila, Dwi Nur'aini Marina Marina Maulida, Fika Melani, Siska Amelia Muhammad Aufa Aslami Muhammad Fauzan MUHAMMAD HABIB Muhammad Ikhwani Mukti Qamal Muliana, Erna Mulyawan, Rizka Munirul Ula Mutammimul Ula Muthmainnah Muthmainnah Muthmainnah Muthmainnah Mutiara Fajar Muzakir Nur Nita, Erlija Nosari, Yulia Nurdin Nurdin Nurfasha , Salsa Nurhasanah Nurhasanah Nurlaila, Rizka Nurmalita Nurmalita Nurul Hayati Harahap Perdinanta, Gita Rafika Rafika Rahayu Rahayu Rahma Fitria Rahma Fitria, Rahma Rahma, Hilmia Rahmah, Chatrine Aulia Razi, Ar Razif, Razif Reza Putra Rizka Mulyawan Rizki Setiawan Rizki, Dini Rizky Putra Fhonna Sahputra, Ilham Salsa Nurfasha Salwan, Defri Sayed Fachrurrazi Sri Wilujeng Susanti Susanti Syakhila, Amanda Syamsul Bahri Sylvie Anastasya Utami Tarigan, Anjasmara Teguh Gunawan Veri Ilhadi Zulnazri, Z Zurhijjah Zurhijjah