Comparing Regularized B-spline Neural Network, Multilayer Perceptron and Boosted-CART on Two Problems of Heart Arrhythmia Diagnosis

Medical diagnosis has special requirements on reliability and interpretability of a learning scheme. Two problems in heart arrhythmia diagnosis are discussed in the paper 1) distinguish premature ventricular contraction beats from normal beats (Problem A); 2) distinguish premature ventricular contraction beats from premature atrial beats (Problem B). Analysis of the real clinical data shows that these two problems have different noise levels, which is suitable for addressing various requirements of medical domain. The performances and characteristics of three methods are compared: Regularized B-spline Neural Networks, Multilayer Perceptron and Boosted-CART using AdaBoost. Regularized B-spline Neural Network outperforms Multilayer Perceptron on the difficult Problem B, which suggests its potential in modeling complex system. Overall, Boosted-CART achieves best recognition rate with intermediate interpretability.