Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Building of Informatics, Technology and Science

Effective Coronary Artery Disease Prediction Using Bayesian Optimization Algorithm and Random Forest Amrullah, Muhammad Syiarul; Yuniarti, Anny
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5554

Abstract

Coronary artery disease (CAD) continues to be a major global health issue, demanding more effective diagnostic techniques. This study introduces a detailed framework for CAD detection that integrates data preprocessing, feature engineering, and model optimization to enhance diagnostic accuracy. Our methodology encompasses comprehensive data cleansing to eliminate inconsistencies, transformations for better feature representation, feature reduction to highlight relevant variables, data augmentation for balanced class distribution, and optimization strategies to boost model performance. We employed a random forest classifier, trained via 5-fold cross-validation, to develop a robust model. The efficacy of this model was tested through two key experiments: firstly, by comparing its performance on preprocessed versus raw data, and secondly, against previous studies. Results demonstrate that our model significantly surpasses the one trained on raw data, achieving an accuracy of 93.00% compared to 86.16%. Moreover, when compared with existing research, our random forest model excels with an accuracy of 93.00%, a F1 Score of 93.00%, and a recall of 94.00%. Despite the superior precision of the Hybrid PSO-EmNN model found in other research, our results are promising. They underscore the potential of advanced feature engineering to further refine the effectiveness of CAD detection models. The study concludes that meticulous data preprocessing and model optimization are crucial for enhancing CAD diagnostics. Future research should focus on incorporating more sophisticated feature engineering techniques and expanding the dataset to improve the model’s precision and overall diagnostic capabilities.
Co-Authors Achmad Chabiburrohman Achmad Fahriza Agus Arifin Agus Arifin, Agus Agus Z. Arifin, Agus Z. Agus Zainal Arifin Agus Zainal Arifin Ahmad Mustofa Hadi Ahmad Mustofa Hadi Ahmad Raihan Muzakki Akira Asano Akira Taguchi Alifiansyah Arrizqy Hidayat Amrullah, Muhammad Syiarul Andi Baso Kaswar Andi Baso Kaswar Anindhita Sigit Nugroho Anindita Sigit Nugroho Anita Hakim Nasution Ardy, Rizky Damara Arif Fathur Mahmuda Arifiani, Siska Arifzan Razak Aris Fanani Aris Tjahyanto Arya Yudhi Wijaya Berlian Rahmy Lidiawaty Betty Natalie Fitriatin Bilqis Amaliah Budi Nugroho Budi Nugroho Chastine Fatichah Christy Atika Sari Darlis Heru Mukti Darlis Herumurti Devira Wiena Pramintya Dhian Satria Yudha Kartika Diana Suteja Dini Adni Navastara, Dini Adni Eva Yulia Puspaningrum Fawwaz Abdulloh Al-Jawi Feni Siti Fauziah2 Fetty Tri A. Fiandra Fatharany Gulpi Qorik Oktagalu Pratamasunu Hadziq Fabroyir Handayani Tjandrasa Hani Ramadhan Hidiyah Ayu Ratna Ma’rufah Hudan Studiawan I Made Satria Bimantara I Made Widiartha I Putu Gede Hendra Suputra Imam Kuswardayan Ishardan Ishardan Isye Arieshanti Kelly Rossa Sungkono Khairun Nisa Kostidjan, Okky Darmawan Lutfiani Ratna Dewi M. Ali Fauzi M. Ali Fauzi Mafazy, Muhammad Meftah Maulana, Hendra MIFTAHOL ARIFIN, MIFTAHOL Mohamad Dion Tiara Muhammad I. Rosadi, Muhammad I. Muhammad Rayyaan Fatikhahur Rakhim Muhammad Riduwan Nadya Anisa Syafa Nafiiyah, Nur Nanik Suciati Nisa', Chilyatun Oviyanti Mulyani Pasnur Pasnur Purwanto, Yudhi Puspitasari, Leny Ratri Enggar Pawening Reginawanti Hindersah Ridho Rahman Hariadi Rindah Febriana Suryawati Sahmanbanta Sinulingga Saiful Bahri Musa Saprina Mamase Saputra, Wahyu Syaifullah Jauharis Siska Arifiani Soegeng Soetedjo Sofyan Sauri, Sofyan Takashi Nakamoto Wahyu Syaifullah Jauharis Saputra Wijayanti Nurul K Wijayanti Nurul Khotimah