Diagnosis of Vertebral Column Pathologies Using Concatenated Resampling with Machine Learning Algorithms
Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms Aijaz Ahmad Reshi1,*, Imran Ashraf2,*, Furqan Rustam3, Hina Fatima Shahzad3, Arif Mehmood4 and Gyu Sang Choi2 1 College of Computer Science and Engineering, Department of Computer Science, Taibah University, Al Madinah Al Munawarah, Saudi Arabia 2 Information and Communication Engineering, Yeungnam University, Gyeongbuk, Gyeongsan-si, South Korea 3 Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan 4 Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan * These authors contributed equally to this work. ABSTRACT Medical diagnosis through the classification of biomedical attributes is one of the exponentially growing fields in bioinformatics. Although a large number of approaches have been presented in the past, wide use and superior performance of the machine learning (ML) methods in medical diagnosis necessitates significant consideration for automatic diagnostic methods. This study proposes a novel approach called concatenated resampling (CR) to increase the efficacy of traditional ML algorithms. The performance is analyzed leveraging four ML approaches like tree-based ensemble approaches, and linear machine learning approach for automatic diagnosis of inter-vertebral pathologies with increased. Besides, undersampling, over-sampling, and proposed CR techniques have been applied to unbalanced training dataset to analyze the impact of these techniques on the accuracy of each of the classification model. Extensive experiments have been conducted to make comparisons among different classification models using several metrics 16 December 2020 Submitted including accuracy, precision, recall, and F1 score. Comparative analysis has been 25 April 2021 Accepted performed on the experimental results to identify the best performing classifier along Published 22 July 2021 with the application of the re-sampling technique.
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