  The diagnosis of heart diseases is a difficult task generally addressed by an
appropriate examination of patients clinical data. Recently, the use of heart
rate variability (HRV) analysis as well as of some machine learning algorithms,
has proved to be a valuable support in the diagnosis process. However, till
now, ischemic heart disease (IHD) has been diagnosed on the basis of Artificial
Neural Networks (ANN) applied only to signs, symptoms and sequential ECG and
coronary angiography, an invasive tool, while could be probably identified in a
non-invasive way by using parameters extracted from HRV, a signal easily
obtained from the ECG. In this study, 18 non-invasive features (age, gender,
left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects
(156 normal subjects and 87 IHD patients) were used to train and validate a
series of several ANN, different for number of input and hidden nodes. The best
result was obtained using 7 input parameters and 7 hidden nodes with an
accuracy of 98.9% and 82% for the training and validation dataset,
respectively.
