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The workflow computes correlation coefficients to identify correlated features, uses mutual information to measure each feature’s relevance, and removes redundant variables to improve efficiency. XGBoost feature importance iteratively selects the top 10 features, achieving about 60% dimensionality reduction. Classification relies on an LGBM model tuned via random hyper-parameter search, with tenfold cross-validation ensembling weighted by balanced accuracy. Results reach 99.45% accuracy and 99.95% AUC for cancer presence, and 93.94% accuracy and 97.81% AUC for cancer-type classification.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/precision-cancer-classification-using-liquid-biopsy-and-advanced-machine-learning-techniques-research-summary/339012/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/precision-cancer-classification-using-liquid-biopsy-and-advanced-machine-learning-techniques-research-summary/339012.png","ImageObject",300,407,{"name":92,"@type":93},"Finn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What biomarkers are used for cancer detection in this study?","Question",{"text":112,"@type":113},"The study uses plasma cfDNA/ctDNA mutations and protein biomarker concentrations from both cancer patients and healthy controls.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the method reduce redundant features?",{"text":117,"@type":113},"It first identifies correlated features using a correlation coefficient, then applies mutual information to assess relevance to the target variable and remove redundancy.",{"name":119,"@type":110,"acceptedAnswer":120},"Which models and strategies are used for classification?",{"text":121,"@type":113},"An LGBM classifier is trained with hyperparameters optimized by random search, and final predictions are produced by ensembling LGBM models from tenfold cross-validation weighted by balanced accuracy.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},339012,1790179372,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},34359740700684,"https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nPrecision cancer classification using liquid biopsy and advanced machine learning techniques  \nAmr Eledkawy1, Taher Hamza1 & Sara El‑Metwally1,2*  \nCancer presents a significant global health burden, resulting in millions of annual deaths. Timely detection is critical for improving survival rates, offering a crucial window for timely medical interventions. Liquid biopsy, analyzing genetic variations, and mutations in circulating cell‑free, circulating tumor DNA (cfDNA/ctDNA) or molecular biomarkers, has emerged as a tool for early detection. This study focuses on cancer detection using mutations in plasma cfDNA/ctDNA and protein biomarker concentrations. The proposed system initially calculates the correlation coefficient to identify correlated features, while mutual information assesses each feature’s relevance to the target variable, eliminating redundant features to improve efficiency. The eXtrem Gradient Boosting (XGBoost) feature importance method iteratively selects the top ten features, resulting in a 60% dataset dimensionality reduction. The Light Gradient Boosting Machine (LGBM) model is employed for classification, optimizing its performance through a random search for hyper‑parameters. Final predictions are obtained by ensembling LGBM models from tenfold cross‑validation, weighted by their respective balanced accuracy, and averaged to get final predictions. Applying this methodology, the proposed system achieves 99.45% accuracy and 99.95% AUC for detecting the presence of cancer while achieving 93.94% accuracy and 97.81% AUC for cancer‑type classification. Our methodology leads to enhanced healthcare outcomes for cancer patients.  \nCancer is widespread globally, resulting in millions of deaths yearly, making it one of the leading causes of mortality worldwide, with approximately 10 million deaths annually1,2. Unfortunately, even in developed countries, cancer-related deaths are expected to rise3. Genetic alterations are a known cause of cancer, leading to uncontrolled cell growth and evolutionary changes4–6. Without systemic therapy, localized cancers can often be successfully treated through surgery alone1. However, once distant metastasis occurs, surgical excision is rarely curative. Detecting cancers before they metastasize to distant sites is a major focus in cancer research. Detecting cancer at an early stage allows for early medical interventions, reducing patient mortality rates7.  \nEarly cancer detection is now accessible through various methods8. A widely adopted method for early cancer identification now involves liquid biopsy, a non-invasive diagnostic approach that detects mutations and genetic variations in circulating cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), and molecular biomarkers9. In particular, protein biomarkers, sourced from blood plasma with minimal invasiveness to patients10, offer specificity and sensitivity for clinical cancer detection, management, and monitoring11. Numerous biomarkers have been introduced for early and late-stage cancer detection across different cancer types12. Employing a multi-analytic test approach, which evaluates mutations in cfDNA, ctDNA, and various protein biomarkers, has the potential to augment early cancer detection through the application of machine learning methodologies and tools13.  \nIn this study, we employ the term ’cancer detection’ to denote the process of identifying the presence of cancer in an individual. Concurrently, we utilize ’cancer classification’ to indicate the process of determining the cancer type. This paper focuses on integrating protein biomarker concentrations and mutations in cfDNA/ ctDNA from both cancer patients and healthy controls to enhance early cancer detection and classification. The proposed methodology employs a rigorous approach, involving several key steps. The approach begins by using the correlation coefficient to identify correla","cbCaiu7Q54hND7eX","https://ap.wps.com/l/cbCaiu7Q54hND7eX","pdf",3188517,15,"English","# Precision cancer classification using liquid biopsy and advanced machine learning techniques\n## Liquid biopsy biomarkers\n## Feature reduction and selection\n## Model training and ensembling\n## Performance results","[{\"question\":\"What biomarkers are used for cancer detection in this study?\",\"answer\":\"The study uses plasma cfDNA/ctDNA mutations and protein biomarker concentrations from both cancer patients and healthy controls.\"},{\"question\":\"How does the method reduce redundant features?\",\"answer\":\"It first identifies correlated features using a correlation coefficient, then applies mutual information to assess relevance to the target variable and remove redundancy.\"},{\"question\":\"Which models and strategies are used for classification?\",\"answer\":\"An LGBM classifier is trained with hyperparameters optimized by random search, and final predictions are produced by ensembling LGBM models from tenfold cross-validation weighted by balanced accuracy.\"}]","Precision cancer classification using liquid biopsy and advanced machine learning techniques - Research summary | PDF",1790027286,38]