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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Pilar Nusa Mandiri Faktor Exacta JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Riset Informatika Jurnal SITECH : Sistem Informasi dan Teknologi Jusikom: Jurnal Sistem Informasi Ilmu Komputer JURSIMA (Jurnal Sistem Informasi dan Manajemen) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences Jurnal AbdiMas Nusa Mandiri RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Jurnal Responsif : Riset Sains dan Informatika Mediastima JURSIMA Journal Medical Informatics Technology Journal Information & Computer (JICOM) Jurnal Sistem Informasi dan Manajemen
Sulistyowati, Daning Nur
Universitas Nusa Mandiri
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Comparison of Naive Bayes and Decision Tree Methods in Breast Cancer Classification Sulistyowati, Daning Nur; Hadianti, Sri; Mayangky, Nissa Almira
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.112

Abstract

The early diagnosis of breast cancer is a critical factor in improving recovery rates and reducing cancer-related mortality. This study aims to compare the performance of two widely used machine learning algorithms in medical data classification Naive Bayes and Decision Tree in detecting breast cancer using the Breast Cancer Wisconsin (Diagnostic) dataset. The dataset consists of 569 samples with 30 numerical features and one target label. The methodology includes data preprocessing, model training, and performance evaluation using six metrics: accuracy, precision, recall, F1-score, AUC, and MCC. Naive Bayes achieved higher performance, with 96.5% accuracy, 97.6% precision, 93.0% recall, 95.2% F1-score, 0.997 AUC, and 0.925 MCC, compared to Decision Tree with 93.9% accuracy, 90.9% precision, 93.0% recall, 92.0% F1-score, 0.936 AUC, and 0.87 MCC. Confusion matrix and ROC curve analyses support these results, particularly in minimizing classification errors. While Decision Tree offers better interpretability, Naive Bayes may be more suitable for early breast cancer detection under similar dataset conditions. Future studies could explore ensemble approaches to combine the strengths of both methods.
Co-Authors Aditya, Mulyadi Ahmad Setiadi Angelia, Helen Ani Oktarini Sari Aziz, Faruq Dwi Arum Ningtyas Dwi Arum Ningtyas Dwiza Riana Eni Heni Hermaliani Fachri Amsury Fahrul Rozi Frieyadie Gata, Windu Haider Haider Hery, Moch Ida Zuniarti, Ida Imam Budiawan Irwansyah Saputra Ismail Hasan Jansen Pratama Setiawan Lumbantobing Jilan Jamilah Kadafi, Abdul Rahman Laureta, Wika Widya Mahendra Gilang Ramadhynara Maruloh Maruloh Mayangky, Nissa Almira Mohammad Badrul Muhamad Angga Maulana Muhammad Salbiyath Munawaroh, Fiqrotul Nanang Ruhyana Nanang Ruhyana Nita Merlina, Nita Norma Yunita Nurajijah Nurajijah Nurman Machmud Prasetio, Arfhan Reza Ema Furi Rifky Permana Ris yanto Risca Lusiana Pratiwi Risma Purnama Sari Sandra, Gardika Santoso Setiawan Saputra, Surya Fajar Septian Septian Septian, Septian Siti Fauziah Siti Fauziah Siti Fauziah Sri Hadianti Tammayyusdin, Aulia Salma Tati Mardiana Taufik Asra Tika Adilah M Yunita, Norma