[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118842-en":3,"doc-seo-118842-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118842,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","PREDICTION LUNG CANCER BASED CRITICAL FACTORS USING MACHINE LEARNING","Lung cancer affects many people worldwide and is associated with poor prognosis and high mortality. By leveraging image recognition and data analytics, computing systems support early detection and risk assessment across multiple cancer types. This study presents a supervised machine learning approach using Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) trained on a dataset from “Data World” containing 1,000 diseases. The models classify lung cancer patients based on critical risk factors, with Random Forest achieving the highest accuracy of 98.507%.","PREDICTION LUNG CANCER BASED CRITICAL FACTORS USING MACHINE  \nLEARNING  \nSherko H. Murad a* , Ardalan H. Awllab, Brzu T. Mohammed c  \na, c Computer Science Department, Kurdistan Technical Institute, Sulaimani, Kurdistan Region, [Iraq-sherko.murad@kti.edu.iq](Iraq-sherko.murad@kti.edu.iq)[ ](Iraq-sherko.murad@kti.edu.iq)b Department of Computer Science, Cihan University Sulaimaniya, Sulaymaniya 46001, Kurdistan Region, Iraq  \n[ardalan.husin@gmail.com](ardalan.husin@gmail.com)  \nReceived: 11 Jan., 2023/ Accepted: 14 May, 2023/ Published: 25 Sep., 2023 [https://doi.org/10.25271/sjuoz.2023.11.3.1105](https://doi.org/10.25271/sjuoz.2023.11.3.1105)  \nABSTRACT:  \nMany people around the world have lung cancer. Lung cancer has a poor prognosis and a high mortality rate. Through image recognition and data analytics, computers can play a significant role in detecting various types of cancer disease.  \nThis paper provides an effective method to predict lung cancer in an early stage with high accuracy ratio. This research proposed data analytics to determine the accuracy ratio of lung cancer patients using supervised machine learning algorithms (Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) . The dataset for this study was obtained from \"Data World,\" which contains 1,000 diseases. Machine learning algorithms enable us to identify lung cancer risk factors, which aid in diagnosing lung cancer. This study shows that those algorithms can classify lung cancer patients, with Random Forest having the highest accuracy of 98.507% .  \nKEYWORDS: Lung cancer, machine learning, SVM, DT, and RF.   \n1. INTRODUCTION  \nCancer can manifest itself in various parts of the human body. Even for a long time, the disease may go unnoticed. According to the WHO, cancer can be avoided if this is the case. Early, adequate recognition (Chaudhori et al., 2021; Khorshid et al., 2021; Fairouz et al.,2021; Zeebaree et al.,2021; Dahkaz et al.,2021; Chauhau et al., 2016) . One of the cancer types that can ultimately cause death is lung cancer, which is also one of the more well-known cancer types. However, if detected early, it is predicted that 15% of lung cancer patients receiving therapy will live for more than five years after their diagnosis. (Junior et al.,2018) . By looking at these parameters, a computer can help diagnose lung cancer. Lung cancer has the greatest mortality rate of all of these illnesses. This disease is predicted to kill approximately 1.7 million people annually (Abdulqade et al.,2020) . Lung cancer has a dismal prognosis and is greatly influenced by the tumor's stage at diagnosis. The two clinicallytreated lung cancer types are non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) (Ibrahim et al., 2020; Singh et al.,2019; Somvansh et al., 2016) . In actuality, it is a malignant tumor marked by the development of cellular tissue that is out of sequence. When cancer cells invade new tissues, the process is known as metastasis. Cancer tends to spread and is incurable if it goes too far; thus, it should be found as early as possible. Lung cancer only shows symptoms in its advanced stages, making it challenging to identify and practically impossible to treat at this stage. Images of the lungs are captured using imaging techniques such as computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), and X-ray. The most widely used imaging method is the CT image technique since it can provide a view without overlapping components. Doctors have a difficult time interpreting and recognizing cancer.  \nMachine learning is required for complex data categorization and decision-making (Faisal et al., 2018; Zeebaree et al., 2018) .  \nMachine learning is classified into two types: supervised machine learning and unsupervised machine learning. Many systems have insufficient detection accuracy, and particular systems must be constructed to attain the highest level of precision. Machine learn","cbCaikkunvO5zGR9","https://ap.wps.com/l/cbCaikkunvO5zGR9","pdf",460789,1,6,"English","en",105,"# INTRODUCTION\n# LITERATURE REVIEW","[{\"question\":\"What machine learning models are used to predict lung cancer in this research?\",\"answer\":\"The study uses supervised machine learning algorithms including Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF).\"},{\"question\":\"Which model achieved the highest accuracy?\",\"answer\":\"Random Forest achieved the highest accuracy ratio, reported as 98.507%.\"},{\"question\":\"How does the study help identify lung cancer risk factors?\",\"answer\":\"The approach uses data analytics and classification to determine which factors most strongly relate to lung cancer, supporting earlier diagnosis.\"}]","PREDICTION LUNG CANCER BASED CRITICAL FACTORS USING MACHINE LEARNING | PDF",1785720574,15,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"prediction-lung-cancer-based-critical-factors-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/prediction-lung-cancer-based-critical-factors-using-machine-learning/118842/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What machine learning models are used to predict lung cancer in this research?","Question",{"text":76,"@type":77},"The study uses supervised machine learning algorithms including Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which model achieved the highest accuracy?",{"text":81,"@type":77},"Random Forest achieved the highest accuracy ratio, reported as 98.507%.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the study help identify lung cancer risk factors?",{"text":85,"@type":77},"The approach uses data analytics and classification to determine which factors most strongly relate to lung cancer, supporting earlier diagnosis.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"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":107,"slug":138},19,"General","general"]