[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120309-en":3,"doc-seo-120309-105":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},120309,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Multi-Class Classification of Genetic Mutation Using Machine Learning Models - Research Article","Distinguishing genetic mutations that contribute to tumor growth is essential for improving cancer treatment outcomes, yet manual labeling is slow and error-prone due to complex biomedical knowledge. The study applies four supervised machine learning algorithms—support vector machine, naïve Bayes, logistic regression, and random forest—to perform multi-class genetic mutation classification. Model effectiveness is evaluated using log-loss and misclassification rate. Logistic regression achieves the best performance with log loss 1.0125 and a 30.97% misclassification rate, supporting more accurate early tumor analysis.","Computational Journal of Mathematical and Statistical Sciences 3(2), 280–315  \nDOI:10.21608/CJMSS.2024.267064.1040  \n[https:](https://cjmss.journals.ekb.eg/)[//](https://cjmss.journals.ekb.eg/)[cjmss.journals.ekb.eg](https://cjmss.journals.ekb.eg/)[/](https://cjmss.journals.ekb.eg/)  \nResearch article  \nMulti-Class Classification of Genetic Mutation Using Machine Learning Models  \nBarikisu Ntiwaa Ankrah 1∗ , Lewis Brew 1 and Joseph Acquah 1  \n1 Department of Mathematical Sciences, Faculty of Engineering, University of Mines and Technology, Tarkwa, Ghana.  \n* Correspondence: [pg-bnankrah9021@st.umat.edu.gh](pg-bnankrah9021@st.umat.edu.gh)  \nAbstract: The challenge of distinguishing genetic mutations that contribute to tumor growth is crucial in cancer treatment. Cancer is responsible for millions of deaths annually, hence the need for early detection of tumors to improve treatment efficacy and survival rates. However, manual classification is prone to errors and inefficiencies due to human limitations and the complexity of domain knowledge, leading to time-intensive processes. In response, machine learning models improve accuracy and efficiency for cancer prognosis and prediction. However, the lack of theoretical understanding of algorithms may limit the interpretability and applicability of results, where insights into model prediction are crucial to making informed decisions, especially in the biomedical domain. To address these challenges, our study employed four supervised machine learning algorithms, namely Support Vector Machine (SVM), Nave Bayes (NB), Logistic Regression (LR), and Random Forest (RF) . The performance of these algorithms was assessed using log-loss and misclassification rates. Logistic regression emerged as the optimal classifier with a log loss of 1.0125 and a misclassification rate of 30.97% .  \nKeywords: Logistic Regression, Cancer, Term Frequency Inverse Document Frequency (TF-IDF), One-hot encoding, Log loss.  \nMathematics Subject Classification: 62J12, 03C45  \nReceived: 3 February 2024; Revised: 29 March 2024; Accepted: 4 April 2024; Published: 26 April 2024 .  \n Copyright: © 2024 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license.  \n1. Introduction  \nAdvancements in genomics and bioinformatics have revolutionized our understanding of genetic mutations and their potential implications for human health and disease. Gene mutations are fundamental genetic changes that can alter the structure and function of genes and play a vital role in various  \nbiological processes, including diseases such as cancer, drug response, and evolutionary adaptations. Cancer is responsible for most fatalities in the developed world and ranks second in the developing world, causing a loss of nearly 8 million lives annually [24] .  \nAccording to [31], cancer is the uncontrolled growth and spread of abnormal cells in the body, which can form tumors. Tumors are either cancerous (malignant) or non-cancerous (benign) . Malignant tumors can invade surrounding tissues and spread to other parts of the body, while benign tumors typically do not spread and can be removed, as shown in Figure 1. Cancer can occur almost anywhere in the body and is caused by disruptions in the process of cell division, often due to genetic mutations. These alterations in cell division are illustrated in Figure 2.  \nFigure 1. Benign vs Malignant Tumor [4]  \n[7] defines genetic mutations as alterations in the DNA sequence that occur randomly, either due to environmental factors or inherited from birth, and can be categorised into two main types. The first type is hereditary or germline mutation, where inherited variants are passed from parent to child and are present throughout a person’s life in virtually every cell in the body. The second type, known as somatic mutation is acquired during a person’s lifetime due to factors such as ultraviolet light, X-rays, cig","cbCaidtsauQovktr","https://ap.wps.com/l/cbCaidtsauQovktr","pdf",2055849,1,36,"English","en",105,"# Introduction\n## Cancer and genetic mutations\n## Machine learning for tumor classification\n# Materials and Methods\n## Supervised learning models\n## Feature representation and evaluation metrics\n# Results and Discussion\n## Model performance using log loss and misclassification rate\n## Best classifier selection\n# Conclusion","[{\"question\":\"Why is genetic mutation classification important for cancer treatment?\",\"answer\":\"Accurate classification helps identify mutations related to tumor growth, enabling earlier detection and improving treatment effectiveness and patient survival. It also supports more informed decision-making in biomedical contexts.\"},{\"question\":\"Which machine learning algorithms are used in this study?\",\"answer\":\"The study employs four supervised algorithms: Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF).\"},{\"question\":\"How is model performance evaluated and which model performs best?\",\"answer\":\"Performance is assessed using log-loss and misclassification rates. Logistic regression is the top classifier with log loss 1.0125 and a 30.97% misclassification rate.\"}]","Multi-Class Classification of Genetic Mutation Using Machine Learning Models - Research Article | PDF",1785729378,91,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"multi-class-classification-of-genetic-mutation-using-machine-learning-models-research-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/multi-class-classification-of-genetic-mutation-using-machine-learning-models-research-article/120309/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is genetic mutation classification important for cancer treatment?","Question",{"text":75,"@type":76},"Accurate classification helps identify mutations related to tumor growth, enabling earlier detection and improving treatment effectiveness and patient survival. It also supports more informed decision-making in biomedical contexts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used in this study?",{"text":80,"@type":76},"The study employs four supervised algorithms: Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF).",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated and which model performs best?",{"text":84,"@type":76},"Performance is assessed using log-loss and misclassification rates. Logistic regression is the top classifier with log loss 1.0125 and a 30.97% misclassification rate.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]