This paper addresses the fractal analysis of mass spectrometry data for the prediction of complex diseases. We studied ovarian and prostate cancers as examples of the analysis. Experimental results show that the fractal dimensions of cancer states distinctively tend to have higher values than those of the control states. High values of the Hurst exponent of the mass spectrometry data under study suggest the persistent behavior of the datasets and the reliability of the fractal dimensions.