Ischemic heart disease detection using selected machine learning methods

Abstract

This article presents a method based on support vector machines (SVMs) and the Osuna–Platt algorithm used to diagnose the ischemic heart disease. It also includes the necessary concepts from the optimization theory which make it possible to formulate the problem for support vectors and the Osuna–Platt algorithm applied. Next, a principal component analysis (PCA) algorithm used is also presented. Heart images acquired using Single Photon Emission Computed Tomography (SPECT) have been used in the experimental part. Results of classifying cardiac SPECT images using SVM, PCA and neural networks are compared here with those obtained using another method of machine learning – CLIP3 – a combination of the decision tree algorithm and the rule induction algorithm. Tests against an SPECT image database have shown that SVMs are generally more accurate and specific, while the PCA algorithm is the most sensitive for all data sets analysed in this research project.

Keywords:

• optimization theory
• Osuna–Platt algorithm
• statistical classification
• cardiac SPECT imaging
• medical image analysis

Keywords:

• 65D18
• 65K05

Notes

When using correlation matrixes, all attributes have the same influence on the result regardless of their variances. If covariance matrixes are used, attributes with higher variance have a greater influence on the result.