Masters Thesis, Computer Science and Engineering
Department, University of South Florida, 2003.
The early detection of cancer is crucial for successful
treatment, and medical researchers have investigated a number of early-diagnosis
techniques. Recently, they have discovered that some cancers affect the
concentration of certain molecules in the blood, which allows early diagnosis
by analyzing the blood mass spectrum. Researchers have developed several
techniques for the analysis of the mass-spectrum curve, and used them for
the detection of prostate, ovarian, breast, bladder, pancreatic, kidney,
liver and colon cancers.
We have continued this work and applied data mining to the diagnosis
of ovarian cancer based on the mass-spectrum curve. We have identified
the most informative points of this curve, and then used decision trees,
support vector machines, and neural networks to determine the differences
between the curves of cancer patients and healthy people.