[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117860-en":3,"doc-seo-117860-105":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},117860,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",7,"Healthcare","Multi Disease Prediction Using HDO Machine Learning Approach - slideshare_145591364","Machine learning-based predictive analytics is increasingly used to support timely clinical decisions, yet reliable disease prediction remains challenging due to data dependence and limitations in screening and healthcare resources. This study targets early detection of high-fatality conditions including breast cancer, diabetes, and heart-related disorders by leveraging classification models. A web application is developed to make illness prediction accessible, using machine learning forecasts to generate risk-oriented outputs for these diseases and support therapeutic testing workflows.","Multi Disease Prediction Using HDO Machine  \nLearning Approach  \nRutuja A Gulhane1, Sunil R Gupta2  \n1Research Scholar, Department ofCSE  \nPRMIT&R, Badnera, MH, India  \n[gulhanerutuja@gmail.com](gulhanerutuja@gmail.com)  \n2Assistant Professor, Department ofCSE  \nPRMIT&R, Badnera, MH, India  \n[sunilguptacse@gmail.com](sunilguptacse@gmail.com)  \nAbstract—Several machine learning approaches can do predictive analytics on vast volumes of information in various sectors. Predictive analytics in health care is a challenging task. Still, it may ultimately aid physicians in making timely judgments about the health and handling of patients based on vast amounts of information. Breast cancer, diabetes, and heart-related disorders cause numerous fatalities worldwide, yet most of these decreases are attributable to an absence of appropriate screenings. The lack of remedial substructure and a short doctor-to-population proportion contribute to the issue above. Following WHO recommendations, physicians' ratio to affected persons should be in some range; India's doctor-to-public proportion indicates a doctor scarcity. Heart, cancer, and diabetes-related disorders pose a significant danger to humanity if not detected initially. Thus, early detection and identification of these disorders may save many lives. Using classification methods based on machine learning, the focus of this effort is to anticipate dangerous illnesses. Diabetes, heart disease, and breast cancer are discussed in this study. To make this effort easy and accessible to the general community, a web application for therapeutic tests has been developed that use machine learning to create illness predictions. In this study, a web application is created for illness prediction that employs the notion of machine learning-based forecasts for illnesses such as breast cancer, diabetes, and cardiovascular sickness.  \nKeywords-Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Hybrid meta-heuristic Technique.  \nI. INTRODUCTION  \nThe use of AI in healthcare has come a long way in the last 50 years. When combined with the vast volumes of data created by healthcare systems, developments in processing power have allowed us to go beyond the limitations of early AI, which focused on creating algorithms that could make decisions that were previously only possible for humans. Appointment scheduling, drug discovery, and disease diagnosis are just some of the many contemporary healthcare applications of AI. Primary care physicians' manual labor has decreased as AI aid has increased, according to a 2016 study. Disease prediction using machine learning AI models is not novel; there are multiple examples of this already. The reliance on data is the area of uncertainty and has not been examined sufficiently. How much information is required for a project is not always made obvious, and instead just recommendations are made. There is no foolproof method; instead, rule of thumbs and educated guesses are the best bets [1] . The \"one in ten rule\" is a rule of thumb that suggests having ten times as many data points as features to reduce the likelihood of over fitting in regression situations. When an AI is overfit, it is modelled too closely on the training data, leading to subpar performance on the test data. Outliers exist  \nin all data, and training models to accurately identify them might be counterproductive when introducing new information. A learning curve, a graph showing how a model improves with added events, can be used to get a sense of how much data is required to solve a problem. However, that is by no means exhaustive. Several techniques exist for optimizing models at varying numbers of events and features, including model selection, feature selection, and parameter tweaking. Because of their inherent ability to learn complicated nonlinear correlations between input and output properties, nonlinear algorithms also require a larger data set. Let's pretend alinear method works w","cbCaim6LB97iVFK0","https://ap.wps.com/l/cbCaim6LB97iVFK0","pdf",293112,1,6,"English","en",105,"# Abstract\n# Introduction\n## AI in healthcare and predictive analytics\n## Data requirements and model learning behavior\n## Motivation: disease burden and early detection needs","[{\"question\":\"Why is predictive analytics in healthcare considered challenging?\",\"answer\":\"It relies heavily on data quality and sufficient information, and determining what data is required is often unclear. There is no foolproof method, and uncertainty persists in model performance and coverage of scenarios.\"},{\"question\":\"Which diseases does the study focus on for prediction?\",\"answer\":\"The study addresses diabetes, heart disease, and breast cancer as key high-risk illnesses for early detection.\"},{\"question\":\"How does the proposed work make predictions available to users?\",\"answer\":\"It develops a web application that uses machine learning-based forecasts to produce illness prediction results for the targeted diseases and support therapeutic testing.\"}]","Multi Disease Prediction Using HDO Machine Learning Approach - slideshare_145591364 | PDF",1785680035,15,{"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},"multi-disease-prediction-using-hdo-machine-learning-approach-slideshare_145591364","",{"@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/multi-disease-prediction-using-hdo-machine-learning-approach-slideshare_145591364/117860/",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},"Why is predictive analytics in healthcare considered challenging?","Question",{"text":75,"@type":76},"It relies heavily on data quality and sufficient information, and determining what data is required is often unclear. There is no foolproof method, and uncertainty persists in model performance and coverage of scenarios.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which diseases does the study focus on for prediction?",{"text":80,"@type":76},"The study addresses diabetes, heart disease, and breast cancer as key high-risk illnesses for early detection.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed work make predictions available to users?",{"text":84,"@type":76},"It develops a web application that uses machine learning-based forecasts to produce illness prediction results for the targeted diseases and support therapeutic testing.","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},[93,97,101,105,110,114,117,122,127,130,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",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"]