Health Monitoring System Essay
1.1 Health Monitoring System
Artificial Neural Networks (ANNs) are subpart of Artificial Intelligence (AI) systems. Their ability to correlate input and corresponding output data, based on vector mapping, has established themselves as a powerful tool in various applications.
ANNs have been connected in different medical fields, constituting themselves as a valuable procedure in clinical practice for example, cardiology, oncology, pathology, endocrinology, radiology, urology, paediatrics and surgery. Pharmaceutical is a field that ANNs can be demonstrated as an intense device to improve current medical systems. So now we are using ANN in health monitoring system. Any diseases cure rate and prognosis depends mainly on the early detection and
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2) Is to predict possible disease from the data set given using MLP with back propagation algorithm.
3) Ability to process large amount of data.
4) Reduce the time for diagnosis.
5) Cost effective treatment using neural network.
6) High accuracy of prediction.
7) To help clinics and health care organisations for diagnosis of dieses at an early stage.
8) People can be checked for disease quickly and painlessly.
CHAPTER: 3
PROPOSED WORK
3.1 METHODOLOGY
The analysis of health monitoring data measured in uncontrolled environment requires several steps in order to guarantee good quality data and reliable interpretation of data.
The general design steps for health monitoring are summarized below:
Fig 3.1: General Steps in ANN Based Medical Diagnosis
The system gets patient's information to predict the diagnosis of a certain disease. After the target diseases are set up, the following step is to appropriately select the features (e.g. symptoms, biochemical analysis, and other information) that give the information needed to discriminate the different health states of the patient. This should be possible in different ways & careful determination of suitable features must be completed in the first stage. After training and verification, the system can be used in practice to predict the diagnosis. At last, the predicted results is assessed by a clinical
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