[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-125592-105":3,"doc-detail-125592-en":81,"detail-sidebar-cat-0-en-105":97},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","framework-of-machine-learning-techniques-for-cancer-prediction","Framework Of Machine Learning Techniques For Cancer Prediction","","Design a machine-learning framework for predicting skin, lung, and other cancer risks using clinical, demographic, and lifestyle variables from a prepared dataset. Supervised learning models including SVMs and artificial neural networks are trained to estimate malignancy likelihood and evaluate performance through metrics such as accuracy, sensitivity, specificity, and area under the curve. The approach supports earlier diagnosis, potentially improving patient outcomes and reducing healthcare costs. It also reflects the expanding integration of AI and ML in modern medical practice, aiding more effective resource allocation.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/framework-of-machine-learning-techniques-for-cancer-prediction/125592/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/framework-of-machine-learning-techniques-for-cancer-prediction/125592.png","ImageObject",300,407,{"name":42,"@type":43},"Himbo","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-22","2026-08-05",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",12,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What cancers does the framework aim to predict?","Question",{"text":63,"@type":64},"The work targets skin cancer and lung cancer, using a dataset that also incorporates broader cancer-related clinical signals.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which supervised learning methods are used to train the model?",{"text":68,"@type":64},"The model is trained with supervised learning approaches such as support vector machines (SVMs) and artificial neural networks (ANNs).",{"name":70,"@type":61,"acceptedAnswer":71},"How is the model’s effectiveness evaluated?",{"text":72,"@type":64},"Effectiveness is assessed using metrics including accuracy, sensitivity, specificity, and area under the curve (AUC).","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},125592,1785900112,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":30,"language":92,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":93,"faqs":94,"seo_title":95,"seo_description":12,"update_tm":80,"read_time":96},687207017582,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Framework Of Machine Learning Techniques For Cancer Prediction  \nI. UMA MAHESWARA RAO  \nAssociate Professor, Dept of IT, Malla Reddy College of Engineering and Technology, Hyderabad, T.S, India  \nG.PRANAV  \nUG Student, Dept of IT, Malla Reddy College of Engineering and Technology, Hyderabad, T.S, India  \nS.HARIKA DURGA  \nUG Student, Dept of IT, Malla Reddy College of Engineering and Technology, Hyderabad, T.S, India  \nSHERE SAMPREETH REDDY  \nUG Student, Dept of IT, Malla Reddy College of Engineering and Technology, Hyderabad, T.S, India  \nAbstract: The goal of this work is to design a machine-learning model that accurately predicts skin, lung, and skin cancer. Cancer-related clinical, demographic, and lifestyle factors were included in the dataset used to train the model. The model was educated with the help of supervised learning methods like SVMs and ANNs. Accuracy, sensitivity, specificity, and area under the curve were only a few of the criteria used to assess the model's efficacy. The results demonstrate that using demographic and clinical data, the model can effectively estimate individuals' cancer risk. Patient outcomes and healthcare costs may both benefit from this project's efforts to diagnose and treat cancer earlier. With AI and ML research at the forefront of academia, many new uses have been found for these technologies in recent years. It's not just some abstract concept out there; it's relevant to our daily lives, too. As this development continues, we see a closer integration of AI into medical practice. Its central premise also considerably reduced the current imbalance in medical distribution and the demand for available resources.  \nKeywords: Cancer Data Set; Support Vector Machine; Target Values;  \nI. INTRODUCTION:  \nCancer is becoming the leading cause of death among humans, particularly middle-aged women. Curing cancer and reducing mortality rates depend on its early diagnosis. In the current context, mammograms are used to detect cancer since they are the most efficient scanning method available. In this effort, machine learning is used to identify cancer cells. Using patient demographics, medical history, and imaging data, machine learning algorithms can estimate the likelihood of malignancy [1] . There may be a correlation between early identification and greater treatment results and survival rates. Treatment outcomes for individual patients may be predicted using machine learning models by analyzing data such as genetic composition and tumor features. This has the potential to result in more targeted and efficient medical care. Clinical decision support systems for cancer detection and therapy may be generated using machine learning algorithms. Better patient outcomes may result from doctors' increased ability to make well-informed choices using this information. Remote monitoring using machine learning models may improve the quality and quantity of cancer patients'follow-up care. Both patient happiness and healthcare expenses may benefit from this. These days, we often classify machine learning as supervised, semisupervised, or unsupervised. Among the most popular are the closest neighbor classifier, the Naive Bayes classifier, the artificial neural network, and the support  \nvector machine (Support Vector Machine) . Among these, ANN has emerged as one of the most talkedabout algorithms in the scientific community. ML is a multi-domain area that may be researched in many other sectors, including but not limited to computer science, health, finance, and others. ML helps enhance its own performance by acquiring new information and skills while also mimicking human learning behavior. The imbalance and inadequate distribution of medical resources, for example, have emerged as major issues in modern healthcare and are often discussed in the media. In this context, it's clear that ML applications are where medical care is headed now [2] . Researchers at the University of Leeds in the UK have been attempt","cbCainVpNeyMtnWG","https://ap.wps.com/l/cbCainVpNeyMtnWG","pdf",500633,"English","# Abstract\n# Introduction\n# Problem Statement\n# Proposed Methodologies","[{\"question\":\"What cancers does the framework aim to predict?\",\"answer\":\"The work targets skin cancer and lung cancer, using a dataset that also incorporates broader cancer-related clinical signals.\"},{\"question\":\"Which supervised learning methods are used to train the model?\",\"answer\":\"The model is trained with supervised learning approaches such as support vector machines (SVMs) and artificial neural networks (ANNs).\"},{\"question\":\"How is the model’s effectiveness evaluated?\",\"answer\":\"Effectiveness is assessed using metrics including accuracy, sensitivity, specificity, and area under the curve (AUC).\"}]","Framework Of Machine Learning Techniques For Cancer Prediction | PDF",8,{"code":4,"msg":82,"data":98},[99,103,107,111,116,121,124,128,133,136,140],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":104,"show_sort_weight":105,"slug":106},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},"Exam",70,"exam",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":113,"show_sort_weight":114,"slug":115},5,"Comic",60,"comic",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":118,"show_sort_weight":119,"slug":120},6,"Technology",50,"technology",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":122,"slug":123},40,"healthcare",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":126,"slug":127},"Research & Report",30,"research-report",{"id":129,"doc_module":4,"doc_module_name":25,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":25,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":137,"doc_module":4,"doc_module_name":25,"category_name":138,"show_sort_weight":137,"slug":139},10,"Lifestyle","lifestyle",{"id":141,"doc_module":4,"doc_module_name":25,"category_name":142,"show_sort_weight":112,"slug":143},19,"General","general"]