Clinical And Pathological Data Of The Medical Practitioner

2315 Words Mar 24th, 2016 10 Pages
Abstract:
In medical field the disease diagnosis is often made based on the knowledge and experience of the medical practitioner. Due to this there are chances of errors, unwanted biases and also takes longer time in accurate diagnosis of disease. In case of heart disease, its diagnosis is most difficult task. It depends on the careful analysis of different clinical and pathological data of the patient by medical experts, which is complicated process. Due to advancement in machine learning, computer and information technology, the researchers and medical practitioners in large extent are interested in the development of automated system for the prediction of heart disease. ECG consists of various waveforms of electric signals of heat .To process the ECG signal we will generate a multi-layered learning algorithm using a Learning vector Quantization (LVQ) neural network with generalized delta rules under supervised learning. The neural network in this system accepts 13 clinical features as input and predicts that there is a presence or absence of heart disease in the patient, along with different performance measures.
Keywords: Heart disease, Learning Vector Quantization(LVQ) , electrocardiogram (ECG), myocardial ischemia , myocardial infarction.

I. INTRODUCTION

The heart is the organ that pumps blood over blood vessels to different body parts, with adequate proportion of oxygen and other essential nutritional…
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