This document discusses using artificial neural networks (ANNs) to predict health outcomes for patients with diabetes or cardiac issues. Specifically, it describes three studies: 1) A study using a multilayer perceptron neural network to predict psychological well-being in diabetic patients based on biological inputs. 2) A study using ANNs to classify heartbeats from electrocardiograms to diagnose cardiac issues more accurately than rule-based expert systems. 3) A study using a three-layer feedforward neural network to distinguish benign and malignant lesions in mammography images to aid breast cancer diagnosis.

1. ABSTRACT
THE APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN CLINICAL MEDICINE BECOMES VERY
POPULAR IN THE DIAGNOSIS AND PREDICTION. THEREFORE A NEURAL NETWORK MODEL USED TO
PREDICT THE MEASURE OF LIFE QUALITY IN DIABETIC PATIENT. TYPICALLY survey METHOD IS USED
TO COLLECT DATA FROM INDIVIDUAL PATIENT OR FROM HEALTH DEPARTMENTS WHERE THESE DATA
USED AS BIOLOGICAL VARIABLE FOR INPUTS SET IN MULTILAYER PERCEPTRON MODEL, WHICH HAS
BEEN TRAINED WITH BACK PROPAGATION THIS HELP THE CLINICIANS TO HAVE MORE EXACT, EASY
USE SYSTEMS, PROCEDURES AND METHODS TO PREDICT AND DIAGNOSE DISEASES AND OFFER A
BETTER LIFE.
INTRODUCTION
BACKGROUND OF ARTIFICIAL
NEURAL NETWORK According to Moul et al. NN explained as
The brain consists of interconnected set ‘Neural network analysis is a computer-based
of nerve cells, or basic information- data processing structure based on processing
processing units, called neurons (Figure 1). elements that function in a manner analogous
The human brain incorporates nearly 10 to that of the human neuron. Input data to the
billion neurons and 60 trillion connections; processing elements are modified by weighted
called synapses. summation and transferred to succeeding
An artificial neural network consists of a layers of processing elements through
number of very simple processors, also called nonlinear transfer functions, eventually
neurons, which are analogous to the resulting in an output answer’ [2].
biological neurons in the brain but in The application of Artificial Neural
different names as shown in (Table1) [5,7]. Networks in clinical medicine becomes very
A neuron received the signal input from popular in the diagnosis and prediction.
Input from neighboring neuron’s outputs Learning capabilities can improve the
then input signals are combined together performance of an intelligent system over
through weighted links and pass to the next time. The most popular approaches to
neuron. The neuron computes the input machine learning are artificial neural
signals and compares the result with a networks and genetic algorithms [1,7].
threshold using transfer function so if the
input is less than threshold then the output is Input Signals Weights Output Signals
-1 but if the input is equal or large than x1
Y
threshold so the output is +1 and the neuron w1
is now activated and transmit the signal [5,7]. x2
w2
Neuron Y Y
Synapse
Synapse Dendrites wn
Axon Y
Axon xn
FIGURE 3 the artificial neuron
Soma
Soma
Dendrites
Synapse
FIGURE 2 the biological neuron [5,7]

2. BACKGROUND OF DIABETES purpose. In which information passes from
MELLITUS the input to the output but the synaptic
weight calculation done from the output layer
towards the input layer [5].
Diabetes mellitus is a chronic metabolic
disorder disease and consider the fourth
biggest cause of death in the world especially
in the developing and industrial countries [4,3].
It produces health problems as cardio
vascular, visual and psychological issues.
BACKGROUND OF MYOCARDIAL
ISCHAEMIA
Myocardial ischemia is one of the most
SCHEMATIC REPRESENTATION OF A FEED-
common cardiac diseases and it is the FORWARD ANN WITH FOUR INPUT NODES,
insufficient blood supply to the heart muscle VECTOR X, ONE HIDDEN LAYER WITH THREE
so early diagnosis is very important by using NODES, VECTOR H, AND TWO OUTPUT NODES,
electrocardiograms (ECGs) that have been VECTOR Y
proposed during the last two decades.
RESULTS
BACKGROUND OF MAMMOGRAPHY
IN BREAST CANCER
APPLICATION FOR DIABETES
Breast cancer is the most common type of
cancer in women, the use of mammography According to Narasingarao et al. a type of
for screening has largely contributed to early validated questions about five biological
detection, treatment and decrease cancer inputs (age, sex, weight, fasting plasma
mortality. Mammogram is an x-ray glucose and bias) was applied to a number of
photograph of the breast. It is probably the 241 diabetic patients individually to predict
most crucial technique for doctors not only to their psychological feelings in terms of
screen for breast cancer, but also to diagnose, depression, anxiety, energy and diabetes
evaluate, and follow people who had breast worry [3]. They found that men show higher
cancer. It is a safe and reasonably accurate satisfaction level with the treatment provided
and has been in use for about 40 years.
to them than the women. Women had higher
social and diabetes worry [3].
RESEARCH METHODS
APPLICATION FOR CARDIOLOGY
A multilayer perceptron (MLP) network is
a feed forward neural network (FFNN) with According to Papaloukas et al., they have
one or more hidden layers. proposed a method that would keep the
accuracy of prediction even with the presence
David Rummelhart and Robert McLelland of noises in the ECGs. Their improvement that
develop back propagation model that is one using ANNs for classification of heart beats
of the most spreading good algorithm for all instead of using rule-based expert systems

3. which suffered from inaccuracy of prediction The whole idea is about decision
due to the presence of noise in the ECG making problem in which a group of previous
recordings [10]. data information is used to approximate a
decision value. This kind of system gives a
potential aid to clinicians for early prediction
APPLICATION FOR RADIOLOGY
and treatment.
An interesting investigation was
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