[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120140-en":3,"doc-seo-120140-105":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":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},120140,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Prediction of Lung Cancer Disease Using Machine Learning Techniques","Supervised machine learning focuses on using external examples to build hypotheses for predicting new instances, particularly through supervised classification. This study evaluates supervised learning algorithms by presenting and comparing Random Forest and Neural Networks for lung cancer classification. Using 310 cases with 15 independent features and 1 dependent target, Python-based experiments are conducted for analysis. Results indicate Random Forest achieves the highest precision and accuracy, outperforming Neural Networks. Kappa statistic and mean square error are considered alongside model creation time, precision, and minimal error requirements.","| ORIGINAL RESEARCH ARTICLE |  | UHD JOURNAL OF SCIENCE AND TECHNOLOGY |\n| --- | --- | --- |\n\nPrediction of Lung Cancer Disease Using Machine Learning Techniques  \nRukhsar Hatam Qadir1, Karwan Mohammed HamaKarim2  \n1Department of Statistics and Informatics, College-of-administration-and-economics, University of Sulaimani, Sulaimani, Kurdistan Region-Iraq, 2Department of Information Technology, College of science and technology, University of Human Development, Sulaimani, Kurdistan Region-Iraq  \nA B S T R A C T  \nThe pursuit of algorithms utilizing external examples to formulate extensive hypotheses predicting the occurrence of novel instances is recognized, as supervised machine learning (SML) . One of the jobs that intelligent systems perform the most frequently is supervised classification. The goal of this work is to evaluate supervised learning algorithms, explain SML classification methodologies, and identify the most effective classification algorithm given the available data. Two distinct machine learning (ML) techniques were examined: Random Forest (RF) and Neural Networks (NN) . The algorithms were implemented using Python for knowledge analysis. For the categorization, 310 cases from a lung cancer data set were employed, with 15 features serving as independent variables and one serving as the dependent variable. In comparison to NN classification methods, RF was found to be the algorithm with the highest precision and accuracy, according to the results. The study reveals that while the kappa statistic and mean square error (MSE) are factors on the one hand, the time required to create a model and precision (accuracy) are factors on the other. Consequently, to have supervised predictive ML algorithms need to be precise, accurate, and minimum error . Thus, as a consequence of the research, weare currently at this analysis. The categorizing of NNs accuracy is 0.75 the MSE is 0.25, The RF classification accuracy is 0.89 and the MSE is 0.21.  \nIndex Terms: Machine Learning, Classifiers, Data Mining Techniques, Data Analysis, Learning Algorithms, Supervised Machine Learning.  \n1. INTRODUCTION  \nArtificial intelligence (AI) is the capacity of a computer system or machine to perform tasks that normally require human intelligence. Description of two types of AI: Artificial narrow intelligence, or Weak AI, is the first and most prevalent type. Narrow intelligence, which encompasses all current AI systems, is task-specific and task-focused. The  \n\n| Access this article online |  |\n| --- | --- |\n| DOI: 10.21928/uhdjst.v8n2y2024 . pp75-83 | E-ISSN: 2521-4217\u003Cbr>P-ISSN: 2521-4209 |\n| Copyright © 2024 Rukhsar Hatam Qadir and Karwan Mohammed HamaKarim. This is an open access article distributed under the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (CC BY-NC-ND 4 .0) |  |\n\nsecond is artificial general intelligence, which is the concept of a system having the capacity to think and act like a human (adaptable intellect) [1] .  \nTo put it simply, AI seeks to increase human capability and efficiency for activities, such as rebuilding nature and regulating society through intelligent machines, with the ultimate objective of achieving a society in which humans and machines live in harmony. Due to its long history, AI has been applied since the 1980s in several important fields, such as computer vision, natural language processing, the study of cognition and reasoning, robotics, game theory, and machine learning (ML) [2] .  \nIT administration is entering a new era with the management of AI. To effectively manage AI, one must coordinate,  \nCorresponding author’s e-mail: Rukhsar Hatam Qadir, [Email: Rukhsar.qadir@univsul.edu.iq](Email: Rukhsar.qadir@univsul.edu.iq)  \n[Received: 02-04-2024 Accepted: 10-10-2024 Published: 17-11-2024](Received: 02-04-2024 Accepted: 10-10-2024 Published: 17-11-2024)  \nUHD Journal of Science and Technology | Jul 2024 | Vol 8 | Issue 2 75  \nRukhsar and Karwan: Prediction of Lung Cancer Di","cbCaicd5Srj9moC0","https://ap.wps.com/l/cbCaicd5Srj9moC0","pdf",1234252,1,9,"English","en",105,"# Abstract\n# Introduction\n## Artificial Intelligence Concepts\n## Supervised vs Unsupervised Learning\n## Machine Learning in Healthcare\n## Data Mining and Knowledge Discovery","[{\"question\":\"What is the main goal of the study on lung cancer prediction?\",\"answer\":\"The work evaluates supervised learning algorithms, explains SML classification methods, and identifies the most effective classifier based on the available dataset.\"},{\"question\":\"Which machine learning techniques are compared in the study?\",\"answer\":\"Random Forest (RF) and Neural Networks (NN) are examined using Python for the analysis.\"},{\"question\":\"What dataset setup is used for lung cancer categorization?\",\"answer\":\"The study uses 310 cases with 15 independent features and one dependent variable for classification.\"}]","Prediction of Lung Cancer Disease Using Machine Learning Techniques | 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