Heart Disease Diagnosis Using Fuzzy Supervised Learning Based on Dynamic Reduced Features

1. Introduction

Medical diagnosis is an important but complicated task that should be performed accurately and efficiently in which its automation would be very useful and advantageous. Unfortunately, all doctors do not possess expertise in every sub specialty. Moreover, they are in many places a scarce resource. However, appropriate computer-based information and/or decision support systems can aid in enhancing medical care and in achieving clinical tests at a reduced cost. Or, efficient and accurate implementation of automated system needs a comparative study of various available techniques. Indeed, most hospitals today employ some kinds of hospital information systems to manage their healthcare or patient data (Obenshain, 2004; Usher, Laakso, James & Rowlands, 2013). These systems typically generate huge amounts of data which take the form of numbers, text, charts and images. Unfortunately, these data are rarely used to support clinical decision making. There is a wealth of hidden information in these data that is largely untapped. The main motivation of our research is to process data in order to get useful information that enables healthcare practitioners to make intelligent clinical decisions.