Abstract
Analysis of disorders in the human body using computational methods had given greater impact for diagnosis and cure. Vibroarthrographic signals are noninvasive modality that considers vibrations that are obtained from the human knee joints as a signal and analyzes the stability of the human knee joint. The signal analysis reaches the complete classification phase using the machine learning strategies in which feature engineering plays a vital role. The clinical cloud is used by the medical organizations to maintain the patient’s record, and the sample data are transferred to the specific diagnosis device where there is a need for cloud security measures. In this article, VAG signals are transferred from the model clinical cloud using security measures and analyzed by considering the number of feature selection and feature extraction strategies which are further justified by the classification results obtained through the machine learning algorithms. This security-enhanced research paves way for the clinicians and researchers to choose the appropriate security measures, feature analysis, and classifiers to obtain better results. A benchmark dataset that has 89 VAG signals are utilized for constructing the feature strategies, and the results are discussed.
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Balajee, A., Murugan, R. & Venkatesh, K. Security-enhanced machine learning model for diagnosis of knee joint disorders using vibroarthrographic signals. Soft Comput 27, 7543–7553 (2023). https://doi.org/10.1007/s00500-023-07934-2
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DOI: https://doi.org/10.1007/s00500-023-07934-2