[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117580-en":3,"doc-seo-117580-105":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},117580,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","HeathDetect7 - Multiple Disease Identification Using Machine Learning","Numerous machine learning models in healthcare emphasize single-disease detection, while demand continues to grow for unified systems that can predict multiple diseases through one interface. HeathDetect7 addresses this gap by analyzing diverse medical datasets to generate personalized risk assessments for COVID-19, brain tumours, breast cancer, heart disease, diabetes, Alzheimer’s, and pneumonia. Using medical imaging data and clinical parameters, the approach supports early intervention. Random Forest, XGBoost, CNN, and VGG-16 are evaluated within a user-friendly workflow to enable proactive healthcare management.","HeathDetect7: Multiple Disease Identification Using Machine Learning  \nA. Chandana1*, S. Sai Ashrith2, R. Jayanth3, M. Prashanth4  \n1,2,3,4Dept. OfCSE, Geethanjali College of Engineering and Technology, JNTU, Hyderabad, India  \n*Corresponding Author: [aemireddychandana@gmail.com](aemireddychandana@gmail.com), Tel.: +91-8688702126  \nReceived: 23/Feb/2024, Accepted: 25/Mar/2024, Published: 30/Apr/2024  \nAbstract—Numerous machine learning models in healthcare focus on single disease detection, yet there's a growing need for systems that predict multiple diseases using a unified interface. This research addresses this gap by leveraging machine learning techniques to analyse diverse medical datasets and provide personalized risk assessments for diseases such as COVID-19, brain tumours, breast cancer, heart disease, diabetes, Alzheimer's, and pneumonia. These diseases are causing many deaths globally, often due to the lack of timely check-ups and medical interventions. This problem is intensified by inadequate medical infrastructure and a low ratio of doctors to the population. By incorporating medical imaging data and clinical parameters, this study offers a comprehensive approach to disease identification, enabling early intervention and improved health outcomes. The project's user-friendly interface allows individuals to input their medical information easily and receive timely assessments. Various classification algorithms, such as Random Forest, eXtreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNN), and Visual Geometry Group-16 (VGG-16), are explored to achieve accurate disease prediction. The ultimate goal is to create a web application that leverages machine learning to forecast several diseases, contributing to proactive healthcare management, and empowering individuals to monitor their health proactively and make informed decisions about their well-being.  \nKeywords— Unified interface, Personalized risk assessments, Clinical parameters, User-friendly interface, Proactive healthcare management, Informed decisions, Medical imaging data, Random Forest, XGBoost, CNN, VGG-16.   \nI. INTRODUCTION  \nIn today's fast-paced world, Artificial Intelligence (AI) has become a game-changer, bridging the gap between human intelligence and machine capabilities. One particularly fascinating area where AI shines is in Computer Vision, which aims to teach machines to see and understand the world just like humans do. Within this exciting landscape, our project focuses on using AI to revolutionize healthcare, particularly in the field of disease detection.  \nHealth issues affect people everywhere, but getting timely and accurate medical care can be a challenge. Traditional healthcare systems often involve long waits and complicated processes, making it hard for people to get the care they need when they need it. Our project aims to change that by using AI to make disease detection faster, easier, and more accessible for everyone.  \nThe main goal of our project is simple: to empower people to take control of their health by giving them quick and easy access to disease detection services right from their homes. By harnessing the power of ML, we're bringing together seven different disease detection into one convenient online platform. Our project aims to revolutionize healthcare by providing a user-friendly platform for disease detection. With a focus on seven prevalent diseases—COVID, brain tumour, breast cancer, heart disease, diabetes, Alzheimer's, and pneumonia—this study offer users a convenient way to assess their health  \nstatus remotely. Our platform employs a diverse range of cutting-edge algorithms, including Convolutional Neural Networks (CNNs), Random Forest, eXtreme Gradient Boosting (XGBoost), and Visual Geometry Group-16 (VGG-16) . These algorithms are meticulously trained on extensive datasets containing medical images, clinical data, and patient information.  \nII. RELATED WORK  \nA. Multiple Disease Prediction Usi","cbCaio4LlpNheucP","https://ap.wps.com/l/cbCaio4LlpNheucP","pdf",873497,1,8,"English","en",105,"# I. Introduction\n# II. Related Work\n## A. Multiple Disease Prediction Using Machine Learning\n## B. Feasible Prediction of Multiple Diseases using Machine Learning","[{\"question\":\"What problem does HeathDetect7 address in healthcare machine learning?\",\"answer\":\"It addresses the need for a unified system that predicts multiple diseases using one interface, instead of focusing only on single-disease detection.\"},{\"question\":\"Which diseases are targeted for risk assessment?\",\"answer\":\"The project targets COVID-19, brain tumours, breast cancer, heart disease, diabetes, Alzheimer’s, and pneumonia.\"},{\"question\":\"What data types and algorithms does the study use?\",\"answer\":\"It incorporates medical imaging data and clinical parameters, exploring Random Forest, XGBoost, CNN, and VGG-16 to improve disease prediction accuracy.\"}]","HeathDetect7 - Multiple Disease Identification Using Machine Learning | PDF",1785677094,20,{"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},"heathdetect7-multiple-disease-identification-using-machine-learning","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/heathdetect7-multiple-disease-identification-using-machine-learning/117580/",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","2026-08-02",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},"What problem does HeathDetect7 address in healthcare machine learning?","Question",{"text":75,"@type":76},"It addresses the need for a unified system that predicts multiple diseases using one interface, instead of focusing only on single-disease detection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which diseases are targeted for risk assessment?",{"text":80,"@type":76},"The project targets COVID-19, brain tumours, breast cancer, heart disease, diabetes, Alzheimer’s, and pneumonia.",{"name":82,"@type":73,"acceptedAnswer":83},"What data types and algorithms does the study use?",{"text":84,"@type":76},"It incorporates medical imaging data and clinical parameters, exploring Random Forest, XGBoost, CNN, and VGG-16 to improve disease prediction accuracy.","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},[93,97,101,105,110,115,118,122,126,129,133],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":29,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":29,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]