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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of ICT Research and Applications Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Ilmu Komputer dan Bisnis PREPOTIF : Jurnal Kesehatan Masyarakat ProBisnis : Jurnal Manajemen
Ignatius Wiseto Prasetyo Agung
Universitas Adhirajasa Reswara Sanjaya (ARS University)
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences

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Breast cancer identification using a hybrid machine learning system Arifin, Toni; Agung, Ignatius Wiseto Prasetyo; Junianto, Erfian; Agustin, Dari Dianata; Wibowo, Ilham Rachmat; Rachman, Rizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3928-3937

Abstract

Breast cancer remains one of the most prevalent malignancies among women and is frequently diagnosed at an advanced stage. Early detection is critical to improving patient prognosis and survival rates. Messenger ribonucleic acid (mRNA) gene expression data, which captures the molecular alterations in cancer cells, offers a promising avenue for enhancing diagnostic accuracy. The objective of this study is to develop a machine learning-based model for breast cancer detection using mRNA gene expression profiles. To achieve this, we implemented a hybrid machine learning system (HMLS) that integrates classification algorithms with feature selection and extraction techniques. This approach enables the effective handling of heterogeneous and high-dimensional genomic data, such as mRNA expression datasets, while simultaneously reducing dimensionality without sacrificing critical information. The classification algorithms applied in this study include support vector machine (SVM), random forest (RF), naïve Bayes (NB), k-nearest neighbors (KNN), extra trees classifier (ETC), and logistic regression (LR). Feature selection was conducted using analysis of variance (ANOVA), mutual information (MI), ETC, LR, whereas principal component analysis (PCA) was employed for feature extraction. The performance of the proposed model was evaluated using standard metrics, including recall, F1-score, and accuracy. Experimental results demonstrate that the combination of the SVM classifier with MI feature selection outperformed other configurations and conventional machine learning approaches, achieving a classification accuracy of 99.4%.
Co-Authors Acep Rohendi Agustin, Dari Dianata Alfaruk, Muhammad Harist Ardi, Wildan Khalifah Bambang Riyanto Erfian Junianto Erliany Syaodih Hidayat, Fajar Mukti Junaedi, Novan Mayangsari, Ariefa Diah Muhammad Rizqi Prawasari, Nindyan Rabbani, Muhammad Ihsan Ramadhan, Fanky Fazdianki Rinaldi Munir Rizal Rachman, Rizal Salam, Regi Sarwono Sutikno Sarwono Sutikno SATRIYAS ILYAS Toni Arifin Wibowo, Ilham Rachmat