[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120958-en":3,"doc-seo-120958-105":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},120958,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning Model Construction and Testing - Anticipating Cancer Incidence and Mortality","Escalating environmental challenges have contributed to increasing cancer incidence, making accurate forecasting of incidence and mortality a key objective for public health. This study builds a machine learning framework using 72,591 records with age, case counts, population size, race, gender, cancer site, and year of diagnosis. Decision trees, random forests, logistic regression, support vector machines, and neural networks are evaluated, yielding testing accuracies of 62.17%, 61.92%, 54.53%, 55.72%, and 62.30%. The results support evidence-based projections while emphasizing sustainable, judicious use of large, complex datasets.","diseases   \nArticle  \nMachine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality  \nYuanzhao Ding   \nCitation: Ding, Y. Machine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality. Diseases 2024, 12, 139 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diseases12070139  \nAcademic Editor: Charat Thongprayoon  \nReceived: 1 June 2024  \nRevised: 24 June 2024  \nAccepted: 29 June 2024  \nPublished: 30 June 2024  \nCopyright: © 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nSchool of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK; [armstrongding@163.com](armstrongding@163.com)  \nAbstract: In recent years, the escalating environmental challenges have contributed to a rising incidence of cancer. The precise anticipation of cancer incidence and mortality rates has emerged as a pivotal focus in scientific inquiry, exerting a profound impact on the formulation of public health policies. This investigation adopts a pioneering machine learning framework to address this critical issue, utilizing a dataset encompassing 72,591 comprehensive records that include essential variables such as age, case count, population size, race, gender, site, and year of diagnosis. Diverse machine learning algorithms, including decision trees, random forests, logistic regression, support vector machines, and neural networks, were employed in this study. The ensuing analysis revealed testing accuracies of 62.17%, 61.92%, 54.53%, 55.72%, and 62.30% for the respective models. This state-of-the-art model not only enhances our understanding of cancer dynamics but also equips researchers and policymakers with the capability of making meticulous projections concerning forthcoming cancer incidence and mortality rates. Considering sustainability, the application of this advanced machine learning framework emphasizes the importance of judiciously utilizing extensive and intricate databases. By doing so, it facilitates a more sustainable approach to healthcare planning, allowing for informed decision-making that takes into account the long-term ecological and societal impacts of cancer-related policies. This integrative perspective underscores the broader commitment to sustainable practices in both health research and public policy formulation.  \nKeywords: cancer; incidence; mortality; artificial intelligence; machine learning; neural network  \n1. Introduction  \nCancer poses a formidable threat to global health and well-being [1], with a staggering estimated 18.1 million new cases and 9.6 million cancer-related deaths occurring worldwide annually [2,3] . Gender disparities are evident, with higher cancer incidence and mortality rates among males compared to females. Approximately 20% of males and 17% of females will experience cancer during their lifetime, while 13% of males and 9% of females will succumb to the disease [2,3] . Accurately predicting cancer incidence and mortality rates is a crucial pursuit in cancer research. Numerous factors, including demographic, lifestyle, environmental, and genetic elements, influence incidence rates. Moreover, healthcare accessibility and quality significantly impact mortality rates [4] . Precise predictions in these areas are vital, empowering policymakers and healthcare providers to design targeted and effective strategies and interventions, thereby combating cancer’s devastating impact on individuals and communities [5] .  \nTraditionally, cancer prediction relied on mathematical calculations [6,7] . This process involved data collection, followed by the development of formulas connecting cancer occ","cbCail7zMb6MbnIc","https://ap.wps.com/l/cbCail7zMb6MbnIc","pdf",3477895,1,14,"English","en",105,"# Introduction\n# Methods and Data\n## Machine Learning Models\n# Results and Accuracy Evaluation\n# Discussion and Implications","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study focuses on anticipating cancer incidence and mortality rates to support public health policy planning.\"},{\"question\":\"What dataset and key variables are used?\",\"answer\":\"It uses 72,591 records with variables including age, case count, population size, race, gender, site, and year of diagnosis.\"},{\"question\":\"Which machine learning algorithms are evaluated and how is performance reported?\",\"answer\":\"Decision trees, random forests, logistic regression, support vector machines, and neural networks are tested, with performance reported as testing accuracies for each model.\"}]","Machine Learning Model Construction and Testing - Anticipating Cancer Incidence and Mortality | PDF",1785733063,35,{"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},"machine-learning-model-construction-and-testing-anticipating-cancer-incidence-and-mortality","",{"@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/machine-learning-model-construction-and-testing-anticipating-cancer-incidence-and-mortality/120958/",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","2026-08-03",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 the study address?","Question",{"text":75,"@type":76},"The study focuses on anticipating cancer incidence and mortality rates to support public health policy planning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and key variables are used?",{"text":80,"@type":76},"It uses 72,591 records with variables including age, case count, population size, race, gender, site, and year of diagnosis.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms are evaluated and how is performance reported?",{"text":84,"@type":76},"Decision trees, random forests, logistic regression, support vector machines, and neural networks are tested, with performance reported as testing accuracies for each model.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]