Mathematical modeling of Quantum Support Vector Machines is renowned for the capability of quantum algorithms endowed with the unique characteristic of quantum computing lying on the pedestal that nowhere else are the fundamental questions of physics intricately connected with the enormous practical implications along with the voluminous efficiency gains and upgraded computational power. By using the phenomena in quantum physics for the creation of new ways of computing, quantum supremacy reveals itself through the ability of a programmable device providing benefits of problem solutions which cannot be solved merely by conventional computers within a reasonable amount of time. Multifractal-based wavelets have been extensively utilized to characterize complex patterns which are existent in different areas such as medicine, signal processing, image processing and other related fields. Accordingly, wavelet shrinkage is one of the fractal and multifractal-based denoising methods that detects the self-similar, regular and significant attributes which may not be identified by the physicians. Fast estimation mathematical modeling conveys the development of precise mathematical models to approximate and estimate the patterns of complex and dynamic systems. Henceforth, in cases where detailed modeling may prove to be cumbersome or computationally-intensive, fast estimation can provide significant benefits in applications of various fields that entail quick estimates for critical decision-making and/or initial analyses. To this end, the current study has provided contributions on two distinctive approaches for the determination of the self-similar and significant attributes to reach accurate and reliable classification of hepatitis as per binary classification of die or live concerning survival. The steps adopted are as follows: the first part involves the application of Multifractal Wavelet Shrinkage (MWS), one of the multifractal analysis methods, on the hepatitis dataset (X). Significant attributes among the other ones in the dataset were identified, which paved the way toward the generation of the new dataset, named MWS-hepatitis dataset \( \left(\hat{X}\right) \) . The second approach is based on the application of Support Vector Machine (SVM), which is one of machine learning methods, and QSVM, one of the Quantum Machine Learning (QML) methods. The experimental results obtained based on the integrative scheme of the study proposed demonstrate the superiority of the multifractal wavelet shrinkage method. Consequently, the classification results, fast estimation and accuracy rates have been observed to be high when probabilistic outputs and separation of class values are taken into consideration. Thus, the study can unveil the underlying notion behind multifractals in studying complexity and revealing the scaling properties besides the power of quantum machine learning that encompasses much theoretical variety and applications.