Cardiac Problem Diagnosis with Statistical Neural Networks and Performance Evaluation by ROC Analysis

Electronic medical imaging technologies are growing rapidly and simplifying diagnosis in medical area. The proper use of this technology requires a better understanding, interpretation, and development of new, efficient algorithms. Processing and recognition techniques of patterns related to these medical devices are becoming more important. Among these techniques artificial neural network structures are very promising in the diagnosis decision support mechanisms. In this paper, it is aimed to present the performance of statistical neural network structures on classifying cardiac problems which are obtained from SPECT (Single Photon Emission Computed Tomography) images. Principal component analysis has been used to overcome excessive dimensionality of data. After classification we used Receiver Operation Characteristics (ROC) analysis to evaluate system performance. Results show that proper neural network based statistical pattern recognition models will play a fundamental role in medical signal processing and image analysis.