چکیده مقاله
Lung cancer remains a leading cause of cancer related mortality globally Early detection is crucial for improving patient outcomes This study proposes a novel machine learning approach to enhance lung cancer diagnosis By employing the Harris Hawks Optimization algorithm for feature selection and subsequently applying K Nearest Neighbors and Support Vector Machines, we achieved 100% accuracy in classifying lung cancer Our findings demonstrate the potential of this hybrid model for early cancer detection, enabling timely interventions
کلیدواژهها
نویسندگان
Mohammad Khodabandeh
Department of Industrial Engineering & Management Systems Amirkabir University of Technology Tehran Polytechnic Tehran, Iran
Mohadeseh Rajabinejad
Department of Industrial Engineering Faculty of Engineering, Kharazmi University Tehran, Iran
Farzane Parande Moghanloo
Department of Industrial Engineering Faculty of Engineering, Kharazmi University Karaj, Iran
Seyed Hamid Reza Pasandideh
Ph D Department of Industrial Engineering Faculty of Engineering, Kharazmi University Tehran, Iran
شیوه ارجاع
Khodabandeh, Mohammad and Rajabinejad, Mohadeseh and Parande Moghanloo, Farzane and Pasandideh, Seyed Hamid Reza,1403,A Novel Hybrid Intelligent System for Lung Cancer Diagnosis: Harris Hawks Optimization and Support Vector Machines Approaches,The 10th International Conference on Industrial and Systems Engineering,Mashhad
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