[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-384432-105":59,"doc-detail-384432-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","a-heuristic-machine-learning-based-optimization-technique-to-predict-lung-cancer-patient-survival","A Heuristic Machine Learning-Based Optimization Technique to Predict Lung Cancer Patient Survival","","A heuristic machine learning–based optimization approach is developed to predict lung cancer patient survival, addressing the need for more accurate survival estimation despite diverse clinical outcomes and complex disease behavior. The study frames survival as two tasks: a binary question of whether survival exceeds five years, and a regression-based model to estimate survival time within five years. Reported results emphasize strong predictive accuracy, including high recall, precision, and mean absolute error within about one month, using Naive Bayes with SSA and derived machine learning challenges.",{"@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/a-heuristic-machine-learning-based-optimization-technique-to-predict-lung-cancer-patient-survival/384432/",{"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/a-heuristic-machine-learning-based-optimization-technique-to-predict-lung-cancer-patient-survival/384432.png","ImageObject",300,407,{"name":92,"@type":93},"hdkawk","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the paper model lung cancer survival prediction?","Question",{"text":112,"@type":113},"It treats survival as two stages: a binary determination of whether survival exceeds five years, followed by regression to estimate survival time within the five-year window.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is the purpose of using the set detection problem in the study?",{"text":117,"@type":113},"It enables learning from sets of nuclei without nuclei-level annotations and supports anticipating diagnostic labels for new nuclei sets at the patient-relevant set level.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning approach and optimization technique are reported?",{"text":121,"@type":113},"The proposed technique combines Naive Bayes with SSA, generating predictions for overall survival time in the lung cancer setting.","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},384432,1792207771,{"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":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},3985747859855,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Hindawi  \nComputational Intelligence and Neuroscience Volume 2023, Article ID 4506488, 9 pages [https://doi.org/10.1155/2023/4506488](https://doi.org/10.1155/2023/4506488)  \nResearch Article  \nA Heuristic Machine Learning-Based Optimization Technique to Predict Lung Cancer Patient Survival  \nSonia Kukreja , 1 Munish Sabharwal , 1 Mohd Asif Shah ,2 and D. S. Gill 3  \n1 School of Computing Science and Engineering, Galgotias University, Greater Noida, India  \n2 Kebri Dehar University, Kebri Dahar, Ethiopia  \n3 School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology,  \nGwangju, Republic of Korea  \nCorrespondence should be addressed to Mohd Asif Shah; [ayanlehassen@kdu.edu.et](ayanlehassen@kdu.edu.et)  \nReceived 11 July 2022; Revised 26 August 2022; Accepted 24 November 2022; Published 2 February 2023  \nAcademic Editor: Abdul Rehman Javed  \nCopyright © 2023 Sonia Kukreja et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nCancer has been a signifcant threat to human health and well-being, posing the biggest obstacle in the history of human sickness. Te high death rate in cancer patients is primarily due to the complexity of the disease and the wide range of clinical outcomes. Increasing the accuracy of the prediction is equally crucial as predicting the survival rate of cancer patients, which has become a key issue of cancer research. Many models have been suggested at the moment. However, most of them simply use single genetic data or clinical data to construct prediction models for cancer survival. Tere is a lot of emphasis in present survival studies on determining whether or not a patient will survive fve years. Te personal issue of how long a lung cancer patient will survive remains unanswered. Te proposed technique Naive Bayes and SSA is estimating the overall survival time with lung cancer. Two machine learning challenges are derived from a single customized query. To begin with, determining whether a patient will survive for more than fve years is a simple binary question. Te second step is to develop a fve-year survival model using regression analysis. When asked to forecast how long a lung cancer patient would survive within fve years, the mean absolute error (MAE) of this technique’s predictions is accurate within a month. Several biomarker genes have been associated with lung cancers. Te accuracy, recall, and precision achieved from this algorithm are 98.78%, 98.4%, and 98.6%, respectively.  \n1. Introduction  \nDue to a close relationship between tumor formation and altered nuclei morphology, nuclear changes have been crucial for cancer diagnosis [1] . Light microscopy (e.g., haematoxylin and eosin) may be used to visually analyze nuclear morphology in clinical diagnosis [2] . In many tumors, pathologists can identify specifc nucleus alterations that may be used to guide their treatment options. Numerous numerical parameters [3] that defne intrinsic morphological qualities of nuclei, such as their size and shape (e.g., perimeter, area, curvature, and symmetry), as well as nuclear texture, are used in computer-aided diagnostic (CAD) systems to quantify the structure of nuclei [4] . Most of the time, diagnostic labels are only provided for tissue samples, not individual nuclei. A predictive model is  \nrequired for the set detection issue in order to learn from sets of nuclei without nuclei-level annotations and to anticipate the diagnostic label for a fresh set of nuclei. When a model has to forecast a patient’s chance of survival based on a set of measurable nuclei, it is known as the “set detection problem”in cancer diagnosis [5] . Training and testing samples in theset detection scenario are sets, each of which comprises a distinct number of unlabeled nucleus images, while in classic image detection, training ","cbCaibrUFNYiNkVM","https://ap.wps.com/l/cbCaibrUFNYiNkVM","pdf",530627,"English","# Introduction\n## Set detection in cancer diagnosis\n## Majority voting and MIL framework\n## Set-level learning with nuclear feature statistics\n## Bag-of-words for set detection","[{\"question\":\"How does the paper model lung cancer survival prediction?\",\"answer\":\"It treats survival as two stages: a binary determination of whether survival exceeds five years, followed by regression to estimate survival time within the five-year window.\"},{\"question\":\"What is the purpose of using the set detection problem in the study?\",\"answer\":\"It enables learning from sets of nuclei without nuclei-level annotations and supports anticipating diagnostic labels for new nuclei sets at the patient-relevant set level.\"},{\"question\":\"Which machine learning approach and optimization technique are reported?\",\"answer\":\"The proposed technique combines Naive Bayes with SSA, generating predictions for overall survival time in the lung cancer setting.\"}]","A Heuristic Machine Learning-Based Optimization Technique to Predict Lung Cancer Patient Survival | PDF",1790263259,23]