Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques
Liangyu Li,
Jing Yang,
Lip Yee Por,
Mohammad Shahbaz Khan,
Rim Hamdaoui,
Lal Hussain,
Zahoor Iqbal,
Ionela Magdalena Rotaru,
Dan Dobrotă,
Moutaz Aldrdery,
Abdulfattah Omar
Affiliations
Liangyu Li
Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia; Health Informatics Laboratory, Cancer Research Institute, Chifeng Cancer Hospital (Second Affiliated Hospital of Chifeng University), Medical Department, Chifeng University, Chifeng City, Inner Mongolia Autonomous Region, 024000, China
Jing Yang
Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
Lip Yee Por
Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
Mohammad Shahbaz Khan
Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, United States
Rim Hamdaoui
Department of Computer Science, College of Science and Human Studies Dawadmi, Shaqra University, Shaqra, Riyadh, Saudi Arabia
Lal Hussain
Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan; Department of Computer Science and Information Technology, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, 13230, Azad Kashmir, Pakistan
Zahoor Iqbal
School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China
Ionela Magdalena Rotaru
Department of Industrial Engineering and Management, Lucian Blaga University of Sibiu, Bulevardul Victoriei 10, Sibiu, 550024, Romania
Dan Dobrotă
Faculty of Engineering, Lucian Blaga University of Sibiu, Bulevardul Victoriei 10, Sibiu, 550024, Romania
Moutaz Aldrdery
Department of Chemical Engineering, College of Engineering, King Khalid University, Abha, 61411, Saudi Arabia
Abdulfattah Omar
Department of English, College of Science & Humanities, Prince Sattam Bin Abdulaziz University, Saudi Arabia
Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.
