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All Journal TEKNIK INFORMATIKA Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Inovasi Teknologi Pendidikan Proceeding SENDI_U Proceeding of the Electrical Engineering Computer Science and Informatics Format : Jurnal Imiah Teknik Informatika Jurnal Informatika Universitas Pamulang JSiI (Jurnal Sistem Informasi) JURNAL TEKNIK JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI JIKA (Jurnal Informatika) Jurnal Inovasi Informatika Journal of Intelligent Computing and Health Informatics (JICHI) International Journal Of Science, Technology & Management (IJSTM) Jurnal Algoritma, Logika dan Komputasi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Minfo Polgan (JMP) Joutica : Journal of Informatic Unisla Jurnal Keuangan dan Akuntansi Terapan (KUAT) Jurnal Pengabdian Pada Masyarakat Prosiding Simposium Nasional Multidisiplin (SinaMu) Prosiding Seminar Nasional Pemberdayaan Masyarakat (SENDAMAS) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics IJISCS (International Journal of Information System and Computer Science)
Angga Aditya Permana
SINTA ID: 6091531 - SCOPUS ID: 57220893679 - Universitas Multimedia Nusantara
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Journal : Journal of Intelligent Computing and Health Informatics (JICHI)

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Enhancing Early Diagnosis of Heart Disease: A Comparative Study of K-NN and Naive Bayes Classifiers Using the UCI Heart Disease Dataset Permana, Angga Aditya; Arsanah, Arsanah
Journal of Intelligent Computing & Health Informatics Vol 5, No 1 (2024): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i1.11251

Abstract

Heart disease remains a leading cause of mortality globally, necessitating accurate predictive models for early detection and intervention. This study conducted a detailed comparative analysis of the K-nearest neighbor (KNN) and naive bayes classifiers using the UCI Repository Heart Disease dataset to determine the most effective algorithm for heart disease prediction. Our results demonstrate that the proposed KNN outperforms naive bayes in terms of several key metrics: KNN achieved an accuracy of 91.25%, which surpasses naive bayes' accuracy of 88.75%. Additionally, KNN exhibited superior precision (92%), recall (90%), and an F1 score (91%) compared to naive bayes, which demonstrated precision of 89%, recall of 87%, and an F1 score of 88%. The findings of this study have substantial practical implications for medical data analysis. The high accuracy and reliability of the KNN algorithm make it a valuable tool for healthcare professionals in the early diagnosis of heart disease. Implementing KNN-based predictive models can enhance patient outcomes by timely and accurate detection of heart disease, facilitating early intervention, and reducing the risk of severe cardiac events. Moreover, the user-friendly interface of the proposed system streamlines the classification process, making it accessible for clinical use. Future research should explore the integration of additional machine learning algorithms and ensemble methods to further improve prediction accuracy. Developing real-time prediction systems integrated with electronic health records (EHR) could revolutionize patient monitoring and proactive healthcare management, ultimately contributing to better patient outcomes and more efficient healthcare delivery.
Co-Authors Abdul Azis, Muhamad Malik Adhi Kusnadi Ahmad Bregas Prakoso Ahmad Rodoni Alina Primasari Priambudi Alma Pertiwi Amanda, Ivan Nur Analekta Tiara Perdana Analekta Tiara Perdana Anggita Nauli Marpaung Aprilia Damayanti Arsanah, Arsanah Bayu Septian Erlangga Bemby fadillah Deden Kusnanda Destriana, Rachmat Dinar Ajeng Kristiyanti Dinar Ajeng Kristiyanti Dinar Ajeng Kristiyanti Elisabet Dela Marcela Eva Sadiah Fahira Fahira Ferdinand, Rico Habib Amna Henny Leidiyana Henry, Amir Acalapati Herdiansah, Arief Husain, Syepry Maulana Irfan Nasrullah Luigi Ajeng Pratiwi Manorang Sihotang Maskurudin, Muhamad Mifta Alliandry Marfino Muhamad Aldi Setiawan Muhammad Dzulfiqar Ramadhan Wibawanto Muhammad Fany Fahrezi Muhammad Fany Fahrezi Muhammad Nabil Yafi Muhammad Wisnu Prayuda Natasya Yuasan Noveriyanti, Dea Nugroho, Antonius Sony Eko Nur'aini, Aliya Nurdiana Handayani Nurdiana Handayani nurnaningsih, Desi Perdana, Analekta Tiara Permana Perdana Putra Pradipta, Allan Putra, Permana Perdana R Taufiq Ramadhan, Glenn Ramadhan, Muhammad Chezar Ramadhan, Yanuardi Eka Ramadhina, Salsabila Ramadhina, Salsabila Raul Andrian Reynaldy, Deva Alfian RIFQI RIADHI Risma Rohmatul Ummah Rohmat Taufik Rohmat Taufiq Romdendine, Muhammad Fahrury Rudi Firmansyah Salsabila Ramadhina Salsabila Ramadhina Santo Fernandi Wijaya Sri Mulyati Sufyan, Ammar Syafiq, Zahra Syifa Fauziyah Tamam, Gusti Syihabuddin Taufiq, R Taufiq, Rochmat Wahyu Aldhi Noviyanto Wijaya, Muhammad Ibnu Wiratama, Jansen Yanuardi Yanuardi