Abstract
In this study, 1 D wavelet and Partial correlation analyses were applied to a data set obtained from patients with Multiple Sclerosis along with a control group of healthy individuals. The analysis is limited to a sample of 139 individuals, 76 being with Relapsing-Remitting Multiple Sclerosis, 38 with Secondary Progressive Multiple Sclerosis, 6 with Primary Progressive Multiple Sclerosis, and 19 being Healthy individuals. It is the main objective of the study to develop a clinical decision support system in order to classify the patients’ diagnostic data based on features gathered from Magnetic Resonance Imaging. The 1-D Continuous Wavelet Transforms are developed to measure the health status of the patients based on features gathered from Magnetic Resonance Imaging and Expanded Disability Status Scale (EDSS). Classification of the Multiple Sclerosis (MS) diagnosis level indicates that it can be used as an important indicator for making decisions to identify MS health status of patients. Our results of relative distribution of three indicators help to identify some differences of “Remitting Relapsing Multiple Sclerosis”, “Secondary Progressive Multiple Sclerosis”, and “Primary Progressive Multiple Sclerosis”. Features like sex, the maximum and minimum lesion sizes, and maximum and minimum values of EDSS scores are widely known and applied in medical studies. This study has fulfilled what lacked in terms of mathematical explanation concerning the significance of such features.
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Acknowledgements
The authors are thankful to Prof. Dr. Rana Karabudak and her team along with Hacettepe University Neurology Department and Radiology Units. Dr. Yeliz Karaca is particularly grateful to Turkish Neurology Association.
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Karaca, Y., Aslan, Z., Siddiqi, A.H. (2017). 1D Wavelet and Partial Correlation Application for MS Subgroup Diagnostic Classification. In: Manchanda, P., Lozi, R., Siddiqi, A. (eds) Industrial Mathematics and Complex Systems. Industrial and Applied Mathematics. Springer, Singapore. https://doi.org/10.1007/978-981-10-3758-0_11
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