Prediction of cardiac end-systolic and end-diastolic diameters in m-mode values using adaptive neural fuzzy inference system

The cardiac, end-systolic and end-diastolic diameters values are very important m-mode cardiac parameters for infant, children, and adolescents, due to growing up body. These parameters, belonging to heart, must be known in order to make a decision about the subject. The expert decision occurs after comparing measured value to hard-copied charts. Hard-copied charts were prepared previously as a result of long statistical studies and these charts depend on a certain region.Our proposed method presents a valid virtual chart for the experts. The proposed method comprises of two stages: (i) data normalization based on euclidean distance (ii) normalized cardiac parameters predicting using adaptive neural fuzzy system. In order to present performance of the proposed method, mean absolute error, absolute deviation and two-fold cross-validation were used. In addition to performance criteria, different common normalization methods, z-score, decimal scaling and minimum–maximum normalization methods were used to compare.In this study, the aim is to create a valid virtual chart which helps the expert during making the decision about predicting end-systolic and end-diastolic cardiac m-mode values. The results were compared with real cardiac parameters by expert with 10 years of medical experience.