Intelligent Decision Support System for Fetal Delivery using Soft Computing Techniques

Background

The expert systems are tremendously used in health care involving predicting and diagnosing a particular disease. Medical expert systems are useful in certain situations where either the case is quite complex or there are no medical experts readily available for patients (Zaheeruddin, Garima 2006,Fahad Shahbaz Khan 2008). In past decades, various methods and systems have been proposed to efficiently expertise from domain experts. The time scales into consideration, such that the variant of disease symptoms in different time scales can be precisely expressed (Gwo-Haur Hwang 2006). The use of a fuzzy expert system to predict the need for advanced neonatal resuscitation efforts in the delivery room. This system relates the maternal medical, obstetric and neonatal characteristics to the clinical conditions of the newborn (Braz J Med Biol Res 2004). The diagnosis and management of neonatal birth injuries has been developed for the use of general practitioners and obstetricians handling large number of deliveries (Veena Kumari, C.2002). The blood pressure and heart rate used for early risk assessment, hypertension complications and as a guide to preventive intervention during pregnancy (Ayala, D.E 2002). The hierarchical network where stage networks are radial basis function networks (HRBFN) and using the nearest neighbor method as decision rule instead of the approximation method used (Ky Van Ha, 1998) . Success of neural networks in medical diagnosis depends not only on the learning mechanisms and network structure but also on the data quality (Cohen, M.E.2002. Beat-to-beat interval (BBI) and symbolic blood pressure (SP) interactions in pregnant women with chronic hypertension in comparison to those in normotensive pregnant (PRE) and non-pregnant women (Voss, A. 2003).