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While traditional approaches focus on protein\/peptide peaks as features, this study explores a novel method of feature extraction by treating MALDI-TOF spectra as one-dimensional signal array further processed as time-series data. This study investigates the use of computational fractal-based analysis to assess the complexity of MALDI-TOF spectra. Fractal analysis, previously successful in glioblastoma diagnosis using magnetic resonance imaging, was applied here to proteomics data. By treating each MALDI spectrum as a time series and calculating its fractal dimension using various algorithms, machine learning models were trained to differentiate between glioblastoma patients and controls. We demonstrate that fractal dimensions are sufficient to obtain accurate models for glioblastoma diagnosis, despite still underperforming when compared to the traditional feature extraction method. We also show that fractals can be used as support features to increase model performance. 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