[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118596-en":3,"doc-seo-118596-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118596,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Breast cancer identification using a hybrid machine learning system - study approach","Breast cancer remains a prevalent malignancy among women, and late diagnosis can severely affect prognosis. This study develops a machine learning model for breast cancer detection using messenger RNA (mRNA) gene expression profiles. A hybrid machine learning system integrates classification algorithms with feature selection and extraction to manage high-dimensional genomic data while reducing dimensionality without losing critical signals. SVM, random forest, naïve Bayes, k-nearest neighbors, extra trees, and logistic regression are assessed with recall, F1-score, and accuracy, achieving 99.4% accuracy for SVM with mutual information feature selection.","Breast cancer identification using a hybrid machine learning  \nsystem  \nToni Arifin1,2, Ignatius Wiseto Prasetyo Agung1,2, Erfian Junianto1,2, Dari Dianata Agustin1,3, Ilham Rachmat Wibowo1,3, Rizal Rachman3  \n1ARS Digital Research and Innovation, Adhirajasa Reswara Sanjaya University, Bandung, Indonesia 2Informatics Study Program, Faculty of Information Technology, Adhirajasa Reswara Sanjaya University, Bandung, Indonesia 3Information System Study Program, Faculty of Information Technology, Adhirajasa Reswara Sanjaya University, Bandung, Indonesia  \nArticle history:  \nReceived Sep 23, 2024 Revised May 31, 2025 Accepted May 24, 2025  \nKeywords:  \nBreast cancer Feature extraction  \nFeature selection Gene expression Identification  \nCorresponding Author:  \nBreast 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% .  \nThis is an open access article under the CC BY-SA license.  \nToni Arifin  \nADRI (ARS Digital Research and Innovation) , Informatics Study Program, Faculty of Information Technology, Adhirajasa Reswara Sanjaya University  \nWest Java, Bandung, Indonesia  \nEmail: [toni.arifin@ars.ac.id](toni.arifin@ars.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nBreast cancer is the most commonly diagnosed malignancy among women, impacting individuals across 157 countries, and is the leading cause of cancer incidence among women worldwide [1] . In 2022, approximately 2.3 million women were diagnosed with breast cancer, with the disease accounting for an estimated 670,000 deaths [2] . Projections for 2024 indicate that nearly 310,720 new cases of invasive breast cancer and 56,500 cases of ductal carcinoma in situ (DCIS) will be diagnosed [3] . As a multifaceted and heterogeneous disease, breast cancer is influenced by a variety of molecular mechanisms, including genetic  \nmutations, epigenetic alterations, and signaling pathways, all of which contribute to its development, progression, and resistance to treatment. This molecular diversity underpins the complexity of breast cancer and highlights the need for personalized therapeutic strategies tailored to the unique biological characteristics of each patient’s tumor. Messenger ribonucleic acid (mRNA) plays a crucial role in these processes, often interacting with other RNA molecules, such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) . Genomic analysis through mRNA expression profiling has proven valuable in identifyin","cbCaianqUSFoSz7x","https://ap.wps.com/l/cbCaianqUSFoSz7x","pdf",491193,1,10,"English","en",105,"# Introduction\n## Breast cancer burden and need for early detection\n## Role of mRNA expression profiling and biomarkers\n## Machine learning for diagnosis and hybrid approaches\n## Objectives and hybrid system overview","[{\"question\":\"Why is early breast cancer detection important in this study?\",\"answer\":\"Early and accurate detection improves survival rates and patient prognosis, which motivates using molecular data and predictive models for timely diagnosis.\"},{\"question\":\"How does the hybrid machine learning system (HMLS) improve analysis of mRNA data?\",\"answer\":\"HMLS combines classification algorithms with feature selection and feature extraction, reducing the high dimensionality of genomic datasets while preserving critical information.\"},{\"question\":\"Which configuration performed best and what metric was reported?\",\"answer\":\"The SVM classifier with mutual information (MI) feature selection achieved the highest performance, reaching 99.4% classification accuracy.\"}]","Breast cancer identification using a hybrid machine learning system - 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