[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118526-en":3,"doc-seo-118526-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118526,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Lung Cancer Prediction System based on Machine Learning Algorithms","Lung cancer remains the most lethal malignant tumor, and early recognition is essential for improving survival outcomes. The study builds an end-to-end lung cancer prediction pipeline using a publicly available Kaggle dataset, applying preprocessing and normalization before splitting data into training and testing sets. Four widely used classifiers—AdaBoost, Decision Tree, Support Vector Machine, and Random Forest—are trained and evaluated with precision, recall, F1 score, and accuracy. Results demonstrate strong performance across low, medium, and high cancer levels, with Random Forest achieving the highest accuracy.","19(3): 95-102, 2024  \n[www.thebioscan.com](www.thebioscan.com)  \nLung Cancer Prediction System based on Machine Learning Algorithms  \nM.Prabu  \nDepartment of Artificial Intelligence and Data Science, Aalim Muhammad Salegh College ofEngineering, Chennai – 600 055, India.  \nDOI: [https://doi.org/10.63001/tbs.2024.v19.i03.pp95-102](https://doi.org/10.63001/tbs.2024.v19.i03.pp95-102)  \nKEYWORDS  \nlung cancer, AdaBoost,  \ndecision tree (DT), support vector machine (SVM),  \nrandom forest classifier (RF) .  \nReceived on:  \n08-08-2024 Accepted on:  \n10-12-2024  \nABSTRACT  \nLung cancer is the most dangerous malignant tumour in terms of morbidity and mortality, and it poses a significant threat to human health. Recognizing and predicting lung cancer at the earliest possible stage can significantly enhance patient survival. Machine learning techniques can predict lung cancer early and effectively. We used a publicly accessible dataset from the Kaggle web repository and employed machine learning algorithms to predict lung cancer. After the pre-processing and normalization procedures on the dataset, the dataset is divided into training and testing subsets. To determine the optimum model for lung cancer prediction, this study employs four prominent classifiers, such as AdaBoost, Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) . In this study, experimental results show that the proposed machine learning algorithms achieve accuracy of 71.67%, 85.67%, 97%, and 100% in predicting three levels (low, medium, and high) of lung cancer. Random Forest classifier outperforms the other classifiers with the highest accuracy. The performance ofclassifiers is compared using parameters such as precision, recall, F1 score, and accuracy.  \nINTRODUCTION  \nLung cancer develops within the lungs and is the main cause of cancer-related deaths in men and women. Nonsmall cell lung cancer (NSCLC) is the predominant type, comprising 80% of all instances of lung cancer. Small-cell lung cancer (SCLC) and mesothelioma are two other types. Smoking is the primary cause of lung cancer. However, other factors such as second-hand smoke, radon, asbestos, and certain chemicals can also increase the risk. Persistent cough, breathlessness, wheezing, chest discomfort, hoarseness, unintentional weight loss, fatigue, bone soreness, recurrent headaches, speech difficulty, and memory lapses are just a few of the symptoms. Anyone experiencing the symptoms listed above should seek medical attention.  \nIn India, lung cancer emerges as the primary contributor to cancer-related mortality, constituting nearly a quarter of all deaths attributed to cancer. Lung cancer is becoming more common in the country, with an increasing occurrence. Notably, it has risen to become the most common cancer among Indian women. Smoking is the primary cause of lung cancer in India, responsible for a large number of cases. In India, approximately 40% of men and 10% of women smoke. Furthermore, over 30% of non-smokers in India are exposed to second-hand smoke, which is one of the major risk factors for lung cancer. Lung cancer is caused by the use of biomass fuels and exposure to certain chemicals increases the risk of lung cancer. In 2020, an estimated 70,275 lung cancer cases were diagnosed in INDIA. In developed countries the 5-year lung cancer survival rate is 20% and in INDIA its about 10% . Indian government has launched many initiatives to reduce the lung cancer issue in the country and the measures include: Raising awareness about the causes and potential risk factors, help the people who are attempting to quit smoking, allocating funds for the development of new treatments, Increasing the availability of high-quality cancer care in rural areas. Early prediction of lung cancer can increase the survival rate. Surgical procedures, radiation therapy, chemotherapy, and targeted therapy are the lung cancer  \ntreatment options. Treatment depends on the stage and type of lung cancer. S","cbCaioynJAlBrH8j","https://ap.wps.com/l/cbCaioynJAlBrH8j","pdf",706620,1,"English","en",105,"# Abstract\n# Introduction\n# Related Work","[{\"question\":\"Which machine learning classifiers are used for lung cancer prediction?\",\"answer\":\"The study evaluates four classifiers: AdaBoost, Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF).\"},{\"question\":\"How is the dataset prepared before training the models?\",\"answer\":\"A Kaggle dataset is preprocessed and normalized, then divided into training and testing subsets before model training.\"},{\"question\":\"How are the classifiers compared and which model performs best?\",\"answer\":\"Models are compared using precision, recall, F1 score, and accuracy. Random Forest provides the highest accuracy in the reported experiments.\"}]","Lung Cancer Prediction System based on Machine Learning Algorithms | PDF",1785683992,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"lung-cancer-prediction-system-based-on-machine-learning-algorithms","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/lung-cancer-prediction-system-based-on-machine-learning-algorithms/118526/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Which machine learning classifiers are used for lung cancer prediction?","Question",{"text":74,"@type":75},"The study evaluates four classifiers: AdaBoost, Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF).","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is the dataset prepared before training the models?",{"text":79,"@type":75},"A Kaggle dataset is preprocessed and normalized, then divided into training and testing subsets before model training.",{"name":81,"@type":72,"acceptedAnswer":82},"How are the classifiers compared and which model performs best?",{"text":83,"@type":75},"Models are compared using precision, recall, F1 score, and accuracy. 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