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A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability

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dc.citedby12
dc.contributor.authorHuang S.en_US
dc.contributor.authorArpaci I.en_US
dc.contributor.authorAl-Emran M.en_US
dc.contributor.authorK?l?�arslan S.en_US
dc.contributor.authorAl-Sharafi M.A.en_US
dc.contributor.authorid56465178700en_US
dc.contributor.authorid35728204400en_US
dc.contributor.authorid56593108000en_US
dc.contributor.authorid57203760014en_US
dc.contributor.authorid57196477711en_US
dc.date.accessioned2024-10-14T03:18:03Z
dc.date.available2024-10-14T03:18:03Z
dc.date.issued2023
dc.description.abstractLung cancer, one of the deadliest forms of cancer, can significantly improve patient survival rates by 60�70% if detected in its early stages. The prediction of lung cancer patient survival has grown to be a popular area of research among medical and computer science experts. This study aims to predict the survival period of lung cancer patients using 12 demographic and clinical features. This is achieved through a comparative analysis between traditional machine learning and deep learning techniques, deviating from previous studies that primarily used CT or X-ray images. The dataset included 10,001 lung cancer patients, and the data attributes involved gender, age, race, T (tumor size), M (tumor dissemination to other organs), N (lymph node involvement), Chemo, DX-Bone, DX-Brain, DX-Liver, DX-Lung, and survival months. Six supervised machine learning and deep learning techniques were applied, including logistic-regression (Logistic), Bayes classifier (BayesNet), lazy-classifier (LWL), meta-classifier (AttributeSelectedClassifier (ASC)), rule-learner (OneR), decision-tree (J48), and deep neural network (DNN). The findings suggest that DNN surpassed the performance of the six traditional machine learning models in accurately predicting the survival duration of lung cancer patients, achieving an accuracy rate of 88.58%. This evidence is thought to assist healthcare experts in cost management and timely treatment provision. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11042-023-16349-y
dc.identifier.epage34198
dc.identifier.issue22
dc.identifier.scopus2-s2.0-85165893878
dc.identifier.spage34183
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85165893878&doi=10.1007%2fs11042-023-16349-y&partnerID=40&md5=7604b884451ec5b86051a20eb2705613
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34124
dc.identifier.volume82
dc.pagecount15
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleMultimedia Tools and Applications
dc.subjectClassical machine learning
dc.subjectDeep learning
dc.subjectDemographic and clinical features
dc.subjectLung cancer
dc.subjectSurvival period prediction
dc.subjectBiological organs
dc.subjectComputerized tomography
dc.subjectDecision trees
dc.subjectDeep neural networks
dc.subjectDiseases
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectPopulation statistics
dc.subjectSupervised learning
dc.subjectTumors
dc.subjectCancer patients
dc.subjectClassical machine learning
dc.subjectClinical features
dc.subjectComparative analyzes
dc.subjectDeep learning
dc.subjectDemographic features
dc.subjectLearning techniques
dc.subjectLung Cancer
dc.subjectMachine-learning
dc.subjectSurvival period prediction
dc.subjectForecasting
dc.titleA comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivabilityen_US
dc.typeArticleen_US
dspace.entity.typePublication
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