[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120352-en":3,"doc-seo-120352-105":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},120352,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Classification of the Condition of Cancer Patients Receiving Home Health Care with Machine Learning Methods","Determining the health status of cancer patients is critical for guiding treatment and improving quality of life. This study analyzes 1,000 home health service patient records prospectively from Amasya University and Research Hospital between January 2013 and August 2017, using Visual Analog Scale (VAS), Karnofsky performance scale, ECOG, and Katz and Bartel scores to classify cancer types. Results from 132 evaluated patients show DT achieves 83.3% accuracy, while SVM and ANN reach 90.2% and 88.6% respectively. Machine learning offers a more sensitive, objective basis for treatment response assessment and supports early diagnosis or risk-group determination.","Düzce University Journal of Science & Technology, 1 (2025) 219-233  \n\n|  | Düzce University\u003Cbr>Journal of Science & Technology\u003Cbr>Research Article |\n| --- | --- |\n| Classification of the Condition of Cancer Patients Receiving Home Health Care with Machine Learning Methods\u003Cbr> Mürsel Kahveci a,*\u003Cbr>a Department of Anesthesia andReanimation, Sabuncuoğlu Şerefeddin Research and Training\u003Cbr>Hospital, Amasya University, Amasya, Turkey\u003Cbr>* Corresponding author’[s e-mail address: drmurselkahveci@yahoo.com](s e-mail address: drmurselkahveci@yahoo.com)\u003Cbr>DOI: 10.29130/dubited.1501760\u003Cbr>ABSTRACT\u003Cbr>Determining the health status of cancer patients is of vital importance in the cancer treatment process. This process plays a critical role in assessing patients' quality of life and supporting the treatment process. We thought that the use of machine learning in the field of cancer treatment and patient care could contribute to better patient outcomes and increased quality of life. Evaluation results of cancer patients who received home health care from Amasya University and Research Hospital between January 2013 and August 2017 were discussed and 1000 patient files in home health service patient records were prospectively examined. In this article, cancer types were classified with machine learning methods using the Visual Analog Scale (VAS), Karnofsky performance scale, ECOG, Katz and Bartel scores to determine the quality of life of cancer patients receiving home health care. This study includes the evaluation results of 132 patients, 69 women (mean age 60.31±9.61) and 63 men (mean age 62.36±9.58) . The DT classifier was noted to exhibit 83.3% accuracy and had the highest sensitivity in the lung cancer type, with a sensitivity of 88.9% . SVM classifier reached the highest accuracy compared to other classifiers with 90.2% accuracy. SVM has the highest sensitivity in lung cancers, with a sensitivity of 97.8% . The ANN classifier achieved 88.6% accuracy for all cancer types.The use of machine learning algorithms may provide a more sensitive and objective way to evaluate patients' response to treatment. Machine learning enables the classification of cancer types by analyzing feature spaces derived from VAS, Karnofsky performance scale, ECOG, Katz, and Bartelscores. This situation can also be constructed as an indicator in early diagnosis or risk group determination, and thus can contribute to improving home health services and increasing the quality of life of cancer patients. The results of this study may contribute to studies aimed at developing more effective strategies for the care and treatment of cancer patients.\u003Cbr>Keywords: Cancer, Deep learning, Machine learning, ANN, SVM, Decision process\u003Cbr>Evde Sağlık Hizmeti Alan Kanser Hastalarının Durumunun Makine Öğrenmesi Yöntemleri ile Sınıflandırılması\u003Cbr>ÖZET\u003Cbr>Kanser hastalarının sağlık durumlarının belirlenmesi, kanser tedavisi sürecinde hayati bir önemesahiptir. Bu süreç, hastaların yaşam kalitesini değerlendirmek ve tedavi sürecini desteklemek içinkritik bir rol oynamaktadır. Biz de makine öğrenmesinin kanser tedavisi ve hasta bakımı alanında kullanılmasının, daha iyi hasta sonuçlarına ve yaşam kalitesinin artırılmasına katkı sağlayabileceğini |  |\n\nReceived: 15/06/2024, Revised: 12/09/2013, Accepted: 27/09/2024 219  \ndüşündük. Ocak 2013-Ağustos 2017 tarihleri arasında XXX Hastanesi’nden evde sağlık hizmeti alan kanser hastalarının değerlendirme sonuçları ele alındı ve Evde sağlık hizmeti alan hasta kayıtlarındaki 1000 hasta dosyası prospektif olarak incelendi. Bu makalede, evde sağlık hizmeti alan, kanser hastalarının yaşam kalitesini belirlemek için Visual Analog Scale (VAS), Karnofsky performans ölçeği, ECOG, Katz ve Bartel skorlarını kullanarak makine öğrenmesi yöntemleriyle kanser türlerisınıflandırıldı . Bu çalışma, 69'u kadın (ortalama yaş 60,31±9,61) ve 63 erkek (ortalama yaş 62,36±9,58) olmak üzere 132 hastanın değerlendirme sonuçlarını içermektedir. DT sınıflandırıcı%83,3","cbCairteS7UqogO9","https://ap.wps.com/l/cbCairteS7UqogO9","pdf",456423,1,15,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"How does the study assess cancer patients receiving home health care?\",\"answer\":\"It uses patient quality-of-life measures including VAS, Karnofsky performance scale, ECOG, and Katz and Bartel scores collected from home health service records.\"},{\"question\":\"Which machine learning models are used to classify cancer types?\",\"answer\":\"The study applies classifiers including DT, SVM, and ANN to predict cancer types based on the derived feature space.\"},{\"question\":\"What do the reported accuracy and sensitivity results indicate?\",\"answer\":\"DT shows 83.3% accuracy with the highest sensitivity in lung cancer, while SVM achieves the highest accuracy (90.2%) and the highest sensitivity for lung cancers (97.8%); ANN reaches 88.6% accuracy across cancer types.\"}]","Classification of the Condition of Cancer Patients Receiving Home Health Care with Machine Learning Methods | PDF",1785729627,38,{"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},"classification-of-the-condition-of-cancer-patients-receiving-home-health-care-with-machine-learning-methods","",{"@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/classification-of-the-condition-of-cancer-patients-receiving-home-health-care-with-machine-learning-methods/120352/",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},"How does the study assess cancer patients receiving home health care?","Question",{"text":75,"@type":76},"It uses patient quality-of-life measures including VAS, Karnofsky performance scale, ECOG, and Katz and Bartel scores collected from home health service records.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used to classify cancer types?",{"text":80,"@type":76},"The study applies classifiers including DT, SVM, and ANN to predict cancer types based on the derived feature space.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the reported accuracy and sensitivity results indicate?",{"text":84,"@type":76},"DT shows 83.3% accuracy with the highest sensitivity in lung cancer, while SVM achieves the highest accuracy (90.2%) and the highest sensitivity for lung cancers (97.8%); ANN reaches 88.6% accuracy across cancer types.","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"]