COMBINATION OF DATAMINING AND
ARTIFICIAL NEURAL NETWORK IN MEDICAL INDUSTRY
M.P.Lokesh
and G.Shrikrishna
Department of Information Technology
V.S.B Engineering College
lokaprasad94@gmail.com
itshrikrishna@gmail.com
Abstract:
Artificial
Intelligence
has
attracted so many researchers these
years due to their increased usage in
many fields including Medical
industry.
Technological
improvements like laser, computers,
ultra sonic imaging etc.., similarly,
Artificial Neural Network is currently
one another important field that has
found its way in biomedical industry.
Introduction:
Data Mining is the process of
semi automatically analyzing large
Database to find useful patterns. It
deals with large volume of data stored
primarily on disk. This new
technology with great values can be
used in medical Industry for
approaching the information in their
Data ware houses where Data ware
houses is a repository of information
gathered from multiple sources stored
in a united Schema in a single site.
Artificial
Neural
Network
is
commonly
referred
as
neural
Network. It is a mathematical model
based on biological neural system. It
is an algorithm for optimization
learning. It works on the principle of
human brain. It has many learning
methods that can be used in medical
industry. Both the Data mining &
Artificial
Neural
Network
is
implemented on hard ware & software
to enhance the ideas from information
source.
Approaches Data Mining:
Data Mining can be approached
through various methods.
Association Rule:
Decision tree can be used to represent
decision making and represent
decision. In Data mining Decision
tree represents the Data.
Artificial Neural Network:
By this rule it checks for the
relationship among the items. The
rule has the form XY. X&Y are sets
of items that do not intersect. It has
two measurements support and
confidence where support measure
what fraction of population satisfies
both confidence measures how often
the consequence is true.
Decision tree learning:
It is a predictive model which
maps observations about an item. The
Decision tree can also be called
classification tree or regression tree.
The tree has its leaf nodes and the
branches specify the relationship
between them.
Leaf node
The brain is a highly complex
component that can organize the
structural constituents known as
neurons to perform operations many
times faster than the digital computer.
Hence Artificial Neural Network is a
model that works like the train to
complete operations in high speed
similar to the real Neurons. Scholars
say A Neural network is a massively
parallel distributed processor made up
of simple processing unit:
1. Knowledge is acquired by the
natural form through a learning
process.
2. Into
neuron
connection
strengths known as synaptic
weights used to store the
acquired data.
branches
Input
N
Input
N
Hidden layer
N
output layer
N
Fig.1 Structure of Decision
tree
Input
N
Input
N
N
 Output
Fig2. Neural network.
Neural Network architecture:
It can be viewed as a weighted
graph. Where the neurons are nodes
and
directed
edges
represent
connection between neurons.
Single layered
Network:
feed
forward
It is a layered Neuron Network
where neurons are organized in the
form of layers. The input layer of
source projects to an output layer of
neurons but not in vice versa. Hence
this network is strictly a feed forward.
Multi layered feed forward:
They have one or more hidden
layers whose computations nodes are
correspondingly
called
hidden
neurons. It is used to interfere
between the external input & net work
output in useful manner.
Recurrent Network:
It has at least one feedback
loop. It consists of single layer of
neurons with each neuron feeding its
output signal back to the input of all
neurons.
Training Neural Networks:
Supervised learning:
It has an external teacher By
that each output unit has a reason ho
input signals. During the learning
process global information may be
required. Its important issue is the
problem of error convergence. That is
the minimization of error between
desired and computed unit values.
Unsupervised learning:
It uses no external teacher. It is
based only on local information. It
itself organizes the data presented to
the network and detects their
collective properties.
Data Mining based on Neural
Network:
They are non linear statistical
Data Modeling tools. They used to
model the complex relationship
between input & output to find
patterns in data. Using Neural
Network as a tool Data ware housing
firms are harvesting information from
data sets in the process known as Data
mining. The difference between data
ware house & ordinary data bases is
that there is actual manipulation &
cross fertilization of data helping user
to make more informed decisions.
Data 
Data 
Target data
Source
Result
Preprocess
Data
Expression Information Mining preprocess data
fig3. Data Mining without Neural
Network
Original datadata preparing
Rule extracting
Useful RulesRule assessment
Fig4. Data Mining based on Neural
Network
Genetic Engineering:
It is a search that mimics the
process of natural evolution. It is
routinely used to generate useful
solution to optimization & search
problems. It belongs to the larges
class of evolutionally algorithms
which
genetic
solutions
to
optimization
problem
using
techniques inspired by natural
evolution such as Inheritance,
mutation, selection, crossover. There
are attempts to combine Genetic
Engineering and Neural Networks.
Given Five Components,
Genetic
Algorithm
according to these steps
operates
1. The population is initialized,
using the procedure in C3. The
result of initialization is as
determined in C2.
2. Each member of the population
is evaluated using the function
in C1.
3. The population under goes
reproduction until a stopping
criterion.
a) One or more parents are chosen
to reproduce. Parents with high
evaluation are favored. The
parameters of C5 can influence
the selection.
b) The operators of C4 are applied
to the parents to produce
children.
c) The children are evaluated and
inserted into the population
Decision Tree Learning is mostly
used in statistics, data mining and
machine learning.
Training
data
Build tree by applying C4
operator to parents & produce
children.
Initial population
Fitness evaluation
Replace
population
Optional
decision
tree
Term
Yes
Selection of
individual
Cross over
2. Evaluation function
3. Initialization procedure
4. Operators
 Mutation
 Cross over
 Gradient
Mutation:
A Mutation operator takes on
parent and randomly changes some of
the entries in its chromosome to
create a child.
Crossover:
It takes two parent and genes
one or two children unbinding some
genetic of each parent.
Gradient:
A Gradient operator takes one
parent and produces a child by adding
to its entries a multiple of the gradient
with respect to evaluation function.
Genetic Algorithm (GA) Procedure
Mutation (operator
applied)
Fig5. Decision Tree
Our Genetic Algorithm
1. Chromosome Encoding
Procedure GA
Begin
Initialize population;
Evaluate termination condition not
satisfied do Begin
Select
parents
from
current
population;
Apply genetic operators to selected
Parents;
Evaluate off spring equal to current
population;
End
End
The next generation starts as follows:
1. Choose individuals according
to rank selection method.
2. Use two point crossover to
exchange genes
3. Perform a bit level mutation to
each off spring
4. Keep parents and all other
individuals
of
current
population.
Randomly Generated
population
Generate new
Generation
Training
Data
Validation
Data
Feature selection
Decision Tree
construction
Decision Tree
Evaluator
Fitness
Competition
Final Decision
Tree
Fig6. GA/Decision Tree Hybrid
Advantages:
1. It is simple to understand and
easy to implement
2. It supports multi objective
optimization
3. It always provides an answer
and gets better with time
4. It can easily exploit previous
solutions
5. It can be combined with other
decision techniques.
Conclusion:
Owing
wide
range
of
applicability of artificial neural
network and their ability to learn
complex and non linear relationship,
neural network are well suited to
solve problems in biomedical
engineering.
References:
1. Data Base System ConceptsAbraham Silberschatz, Henry
F.Kroth, Sudharsan
2. Data Mining & Neural
Network – Ina Kapoor
Sharma