prognostication of deaf and treatment suggestion...

International Journal of Science, Engineering and Technology Research (IJSETR), Volume 4, Issue 8, August-2015

2920 ISSN: 2278 – 7798 All Rights Reserved © 2015 IJSETR

Prognostication of Deaf and Treatment

Suggestion Using K-Means Clustering Algorithm

K.Rajalakshmi1, Dr.S.S.Dhenakaran

2, N.Roobini

3

Department of computer science & engineering,

Alagappa University, Karaikudi

Abstract-Data Mining is the fastest growing field in

computational environment in which new patterns

are discovered from huge volume of data. Data

Mining techniques can be used in different area

including networking, image processing and as well

as in medical field. The medical industry has large

volume of data collections. These data collections

are needed to convert as useful knowledge.

Nowadays, data mining techniques are applied in

medical research to analysis the huge amount of

medical data. Normally medical data mining is used

for prediction. This paper analysis the data sets to

predict the deafness earlier and suggesting

treatments using K-Means algorithm.

Index Terms – Data Mining, Clustering, K-Means

algorithm

I. INTRODUCTION

Data mining is the approach for finding invisible

knowledge from massive amount of data. Since the

patients counting increases the medical databases also expanding every day. The transactions and

analysis of these medical data will be complex

without the computer based analysis system. The

computer based analysis system indicates the

automated medical diagnosis system. This automated

problem identification system supports the medical

experts to make good decision in treatment and

disease. Data mining is the massive areas for doctors

to handle the huge amount of patient’s data sets in

many ways such as make sense of complex

diagnostic tests, interpreting previous results, and combining the different data together. Traditionally

dispensary decision is sculpture by the medical

expert’s observations rather than the knowledge

which obtain from the enormous amount of data. This

computerized diagnosis system leads to increases the

quality of service provided to the patients and

decreases the medical expenses. Healthcare data mining is the maturing research area in data mining

technology. Data mining holds great promising for

healthcare management to allow health system to

systematically use data and analysis to improve the

care and reduce the cost concurrently could apply to

as much as 30% of overall healthcare spending. In

the healthcare management data mining prediction

are playing active role. The healthcare management

should provide better diagnosis and healing to the

patients to accomplish good quality of service.

HEARING LOSS

Hearing impairment is inability of to hear sound

partially or fully. There are three types of hearing

loss. They are conductive hearing loss, sensorineural

hearing loss, and combination of both. Hearing loss

may be caused by different factors. They are as

follows:

Genetic

Disease

Accident Aging

Drugs

Infection

Environment pollution

In India hearing loss is the second most common

cause of disability. Approximately around 63million

that is 6.3% people were suffered from auditory loss.

Early identification and diagnosis of hearing problem

will get proper treatments that may reduce the population of deaf people.



II.DATA MINING

Data mining is the process of joining one or more

data source and derives the new knowledge from that

data collection. The data mining process uses

different techniques.

Classification

Clustering

Association

Regression

Neural Network

The following figure represents various stages of data

mining process.

Fig.1.Data Mining Process

III.CLUSTERING

Clustering is the most important

unsupervised learning classification. The process of

dividing data into similar object is known as clustering. These clustering methods can be classified

as follows:

a) Partitioning method

b) Hierarchical method

c) Density-based method

d) Grid-based method

e) Model-based method

f) Constraint-based method

Fig.2 Clustering

K-Means Algorithm

K-means clustering is partition based clustering

technique. It was introduced by Macqueen in 1967.

This is one of the simplest forms of unsupervised

learning algorithm that used to solve cluster based

problem. In K-means clustering algorithm given data

sets are divided into fixed number of clusters. This

algorithm consists of two phases. They are as

follows:

1. Fix the K value in advance and choose K

center in random manner. 2. Assign data objects to nearest center



Method:

INPUT: Number of desired clusters K and a

database

D= {d1, d2… dn} Containing n data objects.

OUTPUT: A set of K clusters.

Steps:

1. Determine the center of clusters

2. Determine the distance of each data object to the

centroids

3. Set the position of each cluster to mean of all

data points belonging to that cluster

4 .Repeat steps 2&3 until convergence

K-means algorithm uses iterative method that

minimizes the sum of distances from each object to

its cluster centroid. These algorithm processes until

the sum of distance cannot be decreased further.

Advantages of k-means algorithm:

a) The main advantage of the algorithm is

simplicity and its speed which allows

running large datasets.

b) K-Means may be faster than hierarchical

clustering (if K is small).K-Means may

produce tighter clusters than hierarchical

clustering, especially if the clusters are

globular

Euclidean Distance Measure

Mostly commonly used distance measure is

Euclidean distance measure. Normally it suited for

continuous variables. This formal compare two items

on their attributes and determines how they closed to

each other. X, Y is two points.

V.PROPOSED WORK

There are many approaches that have been used for different disease prediction. In some cases, advance

numerical analysis has used to predict the disease but

cluster analysis is used for different types of

predictions mostly.

Fig.3.System Architecture

This paper provides a methodology for predicting the

deafness. By using the technique, K-means clustering

deaf prediction is performed on the patient’s data

which are given by the users in form based environment.

Datasets:

In this system totally 33attributes and nearly 1600

instances are taken for clustering to find out the

results. Table 1(a), 1(b) shows sample data sets.



The K-means clustering algorithm is applied on user

input and results are stored in database and then the

causes, treatments suggestion will provide to the

users. The results will be in three categories they are

as follows:

a. Genetic(Cluster 1)

b. Medication (cluster 2)

c. Accidental (cluster 3)

The k-Means algorithm assigns each point to the

clusterwhose centroid is nearest. The centroid value

is the mean of all the points in the cluster. The data

set has three dimensions and the cluster has two

points X, Y

X = (x1, x2, x3) and

Y = (y1, y2, y3). Then the centroid Cbecomes C = (z1, z2, z3), where

C1=𝒙𝟏+𝒚𝟏

𝟐 , C2=

𝒙𝟐+𝒚𝟐

𝟐 , C3=

𝒙𝟑+𝒚𝟑

𝟐

Fig.4 K-Means algorithm

In this system mainly used clustering for groupingthe attributes. As we take almost 33 attributes such as

age.In this system take various attributes such as age,

diabetes, generation details, family syndrome, middle

ear surgery, gender,blood pressure, chest pain,

etc.This attributes are grouped through K-Means

clustering algorithm. For example took an attribute

such as age and here considered the age of the person

between 0-100. When applying K-means algorithm

on the age data set, it will find the centroid value and

divide it into three clusters.

Here, age will be divided into 3 clusters such as from

0-30,31-60,61-100. It will assign values to each

cluster. The values are as follows

0-30=0

31-60=1

61-100=2

For gender attribute it will divide into groups such as

Male=0 Female=1.Table 2 shows entire data set

values. K-means algorithm will be applied on each and every attribute which are taken. According to

values attributes and their values will be added in a

dataset. Then the system is being ready for

prediction.

1) init clustering[] to get things started.

o Initiate number of clusters as 3

o Initiate cluster centroid as 1,2,3

for cluster1, cluster2, cluster 3

respectively

2) The Updateclustering returns false if there is a

cluster that has no tuples assigned to it parameter

means[][] is really a reference parameter, its

check existing cluster count can omit this check

if InitClustering and UpdateClustering both

guarantee at least one tuple in each cluster

3) Compute distances from current tuple to all k

means (Euclidean distance)

Fig.5 Distance calculation



4) Update means and update the cluster. If no updates stop the loop. Show clusters.



Initial cluster

Centroid (c1):1

Centroid (c2):2

Centroid (c3):3

Data sets values as follows 1,1,1,1,2,2,2,2,1,3,3,3,1,1,1,1,1,2,2,2,2,3,3,3,3,2,1,2,2,2,1

Distance calculation

E.g. .Distance= 32 − 22=5 likewise distance value between clusters and data points is calculate for each data set

value. Then the data points are move to the cluster which has minimum distance. And recalculate the centroid value



Fig .6 shows the resulting data file is “deaf.csv” and includes 1530 instances. As an illustration of performing

clustering in WEKA,it’suse to implementing the K-means algorithm to cluster the patients in this data set, and to

characterize the patients into different cluster. Figure 6 shows the main WEKA Explorer interface with the data file

loaded.

Fig.6 Attribute visualization in weka



Fig.7 cluster produced by weka tool

Fig.7shows the resulting window shows the centroid of each clusters and as well as statistics on the numbers. And

also the percentage of instances assigned to different clusters. Cluster centroids are the mean value for each cluster.

These centroids can be used to characterize the clusters. For example, the centroid for cluster 1 representing people

who has deafness because of genetic factors, like wise cluster 2 representing people has deafness because of

accidental factors and cluster 3 representing people who has deafness because of medication.

Fig.8 Scatters plots of cluster



Fig 8 shows no. of cluster can be decided by the user and for each attribute there is three different

dimensions available (x-axis, y-axis, and color). Different combinations of attributes will result in a visual rendering

of different relationships within each cluster. In the above figure, fixthe cluster number as x-axis, the instance

number (assigned by WEKA) as the y-axis cluster. In this case, by changing the color dimension to other attributes,

it can be seen their distribution within each of the clusters.

CONCLUSION AND FUTURE SCOPE

In this paper, a new idea to predict deafness of people earlier throughK-means clustering algorithm has

introduced. It overcomes the manual prediction of deafness. This technique is suitable for the dynamic databases

where the people input may differ from each other. In future, incremental clustering algorithms can be used to

predict the deafness and can compare them with each other to detect which algorithm among them provide better

accuracy.

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prognostication of deaf and treatment suggestion...

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