[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124124-en":3,"doc-seo-124124-105":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":20,"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},124124,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Evaluating Machine Learning Models for Prostate Cancer Classification Using Gene Expression Profiles from DNA Microarrays","This study evaluates machine learning models for prostate cancer classification using gene expression profiles generated from DNA microarrays. Owing to the high dimensionality of microarray datasets, robust dimensionality reduction is required to select informative genes and remove redundancy. Multiple feature selection methods are applied, including SNR, ReliefF, CC, MI, and others, combined with classifiers such as KNN, SVM, LDA, DTC, Naïve Bayes, and ANN. The best result pairs SNR with LDA, reaching 95% accuracy using six genes and supporting more precise, efficient diagnostic decision-making.","Evaluating Machine Learning Models for Prostate Cancer Classification Using Gene Expression Profiles from DNA Microarrays  \nSara Haddou Bouazza*, Jihad Haddou Bouazza LAMIGEP, EMSI-Marrakech, Morocco  \nAbstract. This study evaluates various machine learning models for classifying prostate cancer using gene expression profiles from DNA microarrays. Due to the high dimensionality of these datasets, effective dimensionality reduction through feature selection is essential to identify and remove redundant genes. We applied multiple feature selection methods, including Signal to Noise Ratio (SNR), ReliefF, Correlation Coefficient (CC), Mutual Information (MI), and several others. These methods were combined with classifiers such as K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Tree Classifier (DTC), Naïve Bayes (NB), and Artificial Neural Network (ANN) .  \nOur results demonstrated that the best combination was the Signal to Noise Ratio with Linear Discriminant Analysis, achieving a classification accuracy of 95% using only six genes. This study underscores the importance of effective feature selection and classifier combination for precise and efficient prostate cancer diagnosis, paving the way for improved personalized healthcare strategies. Future work will focus on validating these findings with larger datasets and exploring advanced machine learning techniques to enhance classification performance further.  \n1 Introduction  \nIn recent years, gene expression analysis has become a vital tool for addressing the complexities of cancer diagnosis and therapeutic research. By examining gene activity, researchers can gain insights into the mechanisms driving cancer onset and progression. Changes in gene expression patterns serve as important indicators for early cancer detection [1] and provide potential targets for drug development, paving the way for more personalized, preventive, and predictive healthcare strategies [2] .  \nAdvancements in biotechnology have introduced tools to measure and analyse gene expression, aiding diagnostic and therapeutic decisions for various cancers, including prostate cancer. DNA microarray technology generates high-dimensional datasets, measuring thousands of gene expressions across a limited number of samples [3, 4] . This high dimensionality often leads to overfitting in machine learning models, making dimensionality reduction essential to isolate the most relevant genes for accurate cancer classification [5] .  \nThe objective of this paper is to evaluate various feature selection methods and their impact on prostate cancer classification. By comparing the effectiveness of different techniques and classifiers, we aim to identify the optimal combination for accurate and efficient diagnosis. We employed several feature selection methods, including Signal to Noise Ratio (SNR), ReliefF, Correlation Coefficient (CC), Mutual  \n* Corresponding author: [sara.hb.sara@gmail.com](sara.hb.sara@gmail.com)  \nInformation (MI), t-Statistics (t-S), Fisher Score, MaxRelevance Min-Redundancy (MRmr), Principal Component Analysis (PCA), Genetic Algorithm (GA), Random Forest (RF), and a hybrid approach combining Support Vector Machines with Recursive Feature Elimination (SVM-RFE) . After selecting the informative genes, we trained classifiers—K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Tree for Classification (DTC), Naïve Bayes (NB), and Artificial Neural Network (ANN)—to categorize new samples as tumor or non-tumor.  \nThis paper is structured as follows: Section 2 provides definitions and details of the feature selection methods and classifiers used. Section 3 presents a comparative analysis of the performance of different feature selection and classification methods for prostate cancer classification. Section 4 discusses the results obtained from the analysis, and Section 5 offers our conclusions and future dir","cbCairTWAzLPHzny","https://ap.wps.com/l/cbCairTWAzLPHzny","pdf",380179,1,6,"English","en",105,"# Introduction\n# Materials and methods\n## Feature Selection Methods","[{\"question\":\"Why is feature selection important for prostate cancer classification with DNA microarrays?\",\"answer\":\"Microarray gene expression data are highly dimensional, which increases overfitting risk. Feature selection reduces noise and retains the most informative genes for more accurate classification.\"},{\"question\":\"Which feature selection method and classifier combination achieved the best performance?\",\"answer\":\"The best combination was Signal to Noise Ratio (SNR) with Linear Discriminant Analysis (LDA). It reached 95% classification accuracy using only six genes.\"},{\"question\":\"What kinds of machine learning classifiers were evaluated in the study?\",\"answer\":\"The study trained multiple classifiers including KNN, SVM, LDA, Decision Tree Classifier (DTC), Naïve Bayes (NB), and Artificial Neural Network (ANN) to classify samples as tumor or non-tumor.\"}]","Evaluating Machine Learning Models for Prostate Cancer Classification Using Gene Expression Profiles from DNA Microarrays | PDF",1785820596,15,{"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},"evaluating-machine-learning-models-for-prostate-cancer-classification-using-gene-expression-profiles-from-dna-microarrays","",{"@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/evaluating-machine-learning-models-for-prostate-cancer-classification-using-gene-expression-profiles-from-dna-microarrays/124124/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is feature selection important for prostate cancer classification with DNA microarrays?","Question",{"text":75,"@type":76},"Microarray gene expression data are highly dimensional, which increases overfitting risk. Feature selection reduces noise and retains the most informative genes for more accurate classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which feature selection method and classifier combination achieved the best performance?",{"text":80,"@type":76},"The best combination was Signal to Noise Ratio (SNR) with Linear Discriminant Analysis (LDA). It reached 95% classification accuracy using only six genes.",{"name":82,"@type":73,"acceptedAnswer":83},"What kinds of machine learning classifiers were evaluated in the study?",{"text":84,"@type":76},"The study trained multiple classifiers including KNN, SVM, LDA, Decision Tree Classifier (DTC), Naïve Bayes (NB), and Artificial Neural Network (ANN) to classify samples as tumor or non-tumor.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]