Now days most of the research area are working on data mining techniques in medical data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, decision tree, machine learning algorithms like Support Vector Machines, Principle Component Analysis etc., Rough Set Theory and Fuzzy logic. In particular we consider a case study using classification techniques on a medical data set of diabetic patients

Sant, Anjali

Shrivastava, Shiv Shakti

English

Binary Journal of Data Mining & Networking

  
 
Binary Journal of Data Mining & Networking 4 (2014) 29-32  
http://www.arjournals.org/index.php/bjdmn/index 
 
Research Article 
             
Survey: Data Mining Techniques in Medical Data Field    
Shiv Shakti Shrivastava1 , Anjali Sant2                                             
 
*Corresponding author: 
Shiv Shakti Shrivastava  
1Mewar University, Chittorgarh, 
Rajasthan. India 
2Dept of Mathematics, BIST, Bhopal, 
India 
 
 
 
A b s t r a c t  
Now days most of the research area are working on data mining techniques in medical data. 
Knowledge discovery and data mining have found numerous applications in business and scientific 
domain. Valuable knowledge can be discovered from application of data mining techniques in 
healthcare system. In this study, we briefly examine the potential use of classification based data 
mining techniques such as Rule based, decision tree, machine learning algorithms like Support 
Vector Machines, Principle Component Analysis etc., Rough Set Theory and Fuzzy logic. In 
particular we consider a case study using classification techniques on a medical data set of diabetic 
patients. 
Keywords : Healthcare, health data, medical diagnosis, data mining, knowledge discovery in 
databases (KDD) 
Introduction 
Problem of showing uncertain data in economics, engineering, 
environmental science, sociology, medical science, and many 
other fields are very important for solving practical problems [1]. 
The evolution of the mass of data and number of existing 
databases far exceeds the skill of humans to analyze this data, 
which makes both a need and an opportunity to extract knowledge 
from databases. Medical databases have gathered large quantities 
of information about patients and their medical conditions. 
Relationships and patterns within this data could provide new 
medical knowledge. 
It is well known that in Information Technology (IT) driven society, 
knowledge is one of the most significant assets of any 
organization. The role of IT in health care is well established. 
Knowledge Management in Health care offers many challenges in 
creation, dissemination and preservation of health care knowledge 
using advanced technologies. Pragmatic use of Database systems, 
Data Warehousing and Knowledge Management technologies can 
contribute a lot to decision support systems in health care. 
Knowledge discovery in databases is well-defined process 
consisting of several distinct steps. Data mining is the core step, 
which results in the discovery of hidden but useful knowledge from 
massive databases. A formal definition of Knowledge discovery in 
databases is given as follows: 
„Data mining is the non trivial extraction of implicit previously 
unknown and potentially useful information about data‰[8]. 
Data mining technology provides a user - oriented approach to 
novel and hidden patterns in the data. The discovered knowledge 
can be used by the healthcare administrators to improve the quality 
of service. The discovered knowledge can also be used by the 
medical practitioners to reduce the number of adverse drug effect, 
to suggest less expensive therapeutically equivalent alternatives. 
Following are some of the important areas of interests where data 
mining techniques can be of tremendous use in health care 
management.  
Traditionally, decision making in health care is based on the 
ground information, lessons learnt in the past resources and funds 
constraints. However, data mining techniques and knowledge 
management technology can be applied to create knowledge rich 
health care environment. 
Motivation 
 
The main challenges in medical data classification are its huge size 
and its vagueness. Many researchers are trying to achieve the 
higher accuracy for medical data classification by reducing or 
selecting the informative data among the thousands of data. For 
cancer classification, researchers have used some machine 
learning algorithms like Support Vector Machines, Principle 
Component Analysis etc., Rough Set Theory and Fuzzy logic. In 
this paper we have applied bijective soft set based for classification 
for breast cancer, Pima Indians. However there can be a concern 
of patient privacy. It is more than clear that the role of data mining 
is not to practice medicine but to improve useful information and 
knowledge so that better treatment and health care be provided. 
Data Mining & Techniques 
Knowledge discovery in medical databases: Data mining is an 
essential step of knowledge discovery. In recent years it has 
attracted great deal of interest in Information industry [2,10]. 
Knowledge discovery process consists of an iterative sequence of 
data cleaning, data integration, data selection, data mining pattern 
recognition and knowledge presentation. In particulars, data mining 
may accomplish class description, association, classification, 
clustering, prediction and time series analysis. Data mining in 
contrast to traditional data analysis is discovery driven. Data mining 
is a young interdisciplinary field closely connected to data 
ISSN: 2229 -7170
  
This work is licensed under a Creative Commons Attribution 3.0 License.  
 Shrivastava et al. Binary Journal of Data Mining & Networking 4 (1) 29-32 [2014] 
 
PAGE | 30 |
 
 
warehousing, statistics, machine learning, neural networks and 
inductive logic programming.  
Data mining provides automatic pattern recognition and attempts to 
uncover patterns in data that are difficult to detect with traditional 
statistical methods. Without data mining it is difficult to realize the 
full potential of data collected within healthcare organization as 
data under analysis is massive, highly dimensional, distributed and 
uncertain. 
Massive healthcare data needs to be converted into information 
and knowledge, which can help control, cost and maintains high 
quality of patient care. Healthcare data includes Patient centric 
data and Aggregate data. 
For health care organization to succeed they must have the ability 
to capture, store and analyze data. Online analytical processing 
(OLAP) provides one way for data to be analyzed in a multi-
dimensional capacity. With the adoption of data warehousing and 
data analysis/OLAP tools, an organization can make strides in 
leveraging data for better decision making [3]. 
Many healthcare organizations struggle with the utilization of data 
collected through an organization online transaction processing 
(OLTP) system that is not integrated for decision making and 
pattern analysis. For successful healthcare organization it is 
important to empower the management and staff with data 
warehousing based on critical thinking and knowledge 
management tools for strategic decision making. Data warehousing 
can be supported by decision support tools such as data mart, 
OLAP and data mining tools. A data mart is a subset of data 
warehouse.  
It focuses on selected subjects. Online analytical processing 
(OLAP) solution provides a multi-dimensional view of the data 
found in relational databases. With stored data in two-dimensional 
format OLAP makes it possible to analyze potentially large amount 
of data with very fast response times and provides the ability for 
users to go through the data and drill down or roll up through 
various dimensions as defined by the data structure. 
With the widespread use of medical information systems including 
databases, there is an explosive growth in their sizes, Physicians 
are faced with a problem of making use of stored data. The 
traditional manual data analysis has become insufficient and 
methods for efficient computer assisted analysis indispensable. A 
Data Warehouse is a semantically consistent data store that serves 
as a physical implementation of a decision support data model and 
stores the information on which an enterprise needs to make 
strategic decisions[3]. A data warehouse is also often viewed as 
architecture constructed by integrating data from multiple 
heterogeneous sources to support structured and/or ad- hoc 
queries, analytical reporting and decision making. 
Data mining techniques in health care: There are various data 
mining techniques available with their suitability dependent on the 
domain application. Statistics provide a strong fundamental 
background for quantification and evaluation of results. However, 
algorithms based on statistics need to be modified and scaled 
before they are applied to data mining. 
Classification data mining techniques: We now describe a few 
Classification data mining techniques with illustrations of their 
applications to healthcare. 
Rule induction: is the process of extracting useful Âif-thenÊ rules 
from data based on statistical significance. A Rule based system 
constructs a set of if-then-rules. Knowledge represents has the 
form. 
K- Mean Algorithm  
The k-means algorithm is a simple iterative method to partition a 
given dataset into a user- specified number of clusters, k. This 
algorithm has been discovered by several researchers across 
different disciplinesGray and Neuhoff [6] provide a nice historical 
Back ground for k-means placed in the larger context of hill-
climbing algorithms. The algorithm operates on a set of d-
dimensional vectors, D = {xi | i = 1, . . . , N }, where xi ? d denotes 
the ith data point. The algorithm is initialized by picking k points in d 
as the initial k cluster representatives or ―centroids?. Techniques 
for selecting these initial seeds include sampling at random from 
the dataset, setting them as the solution of clustering a small 
subset of the data or perturbing the global mean of the data k 
times. 
Association Rule Mining (Arm) 
ARM is a popular data miring technique, which aims at discovering 
strong interesting patterns (associations) between items in vast 
data sets. For instance, X =$ Y, read „X implies YlÊ,is an 
association rule, which is interpreted as „if X occurs it is most likely 
that Y also will occur‰. The task of mining asslociation rules is 
simply searching for items which occur frequently (large items), 
defined by a user-defined minimum frequency (minimum support), 
and then finding patterns in their occurrences. Various algorithms 
exist to efficiently search for and count large item sets. Hipp et al. 
[14] provide a detailed slulrey of ARM algorithms. A number of 
them have bottlenecks, which limit the size and nature of datasets 
they can efficiently mine. For instance, generating candidate items 
sets have always been a source of bottleneck in the 
implementation of Apriori-like ARM algorithms in large data sets. 
Another problem encountered is the I/O overhead, which occurs 
when the transaction database exceeds the size of main memory.  
Rule Induction Method has the potential to use retrieved cases for 
predictions. The following example gives rule induction method for 
prediction blood alcohol concentration. This is only for example 
purpose. 
Table 1: Attributes for alcohol measurement  
Attributes  
Age (in yrs) Meal (empty stomach, lunch, full)
Sex (M/F) Amount of alcohol (ethanol in 
units) 
Mass (in kg) Blood_alcohol content (high/low)
Tobacco_use Blood_pressure(high/low)
Height (in cm) Time_duration (time spent 
drinking) 
 Shrivastava et al. Binary Journal of Data Mining & Networking 4 (1) 29-32 [2014] 
 
PAGE | 31 |
 
 
Decision tree: It is a knowledge representation structure consisting 
of nodes and branches organized in the form of a tree such that, 
every internal non-leaf node is labeled with values of the attributes. 
The branches coming out from an internal node are labeled with 
values of the attributes in that node. Every node is labeled with a 
class (a value of the goal attribute). Tree-based models which 
include classification and regression trees, are the common 
implementation of induction modeling[5]. Decision tree models are 
best suited for data mining. They are inexpensive to construct, 
easy to interpret, easy to integrate with database system and they 
have comparable or better accuracy in many applications. There 
are many Decision tree algorithms such as HUNTS algorithm (this 
is one of the earliest algorithm), CART, ID3, C4.5 (a later version 
ID3 algorithm), SLIQ, SPRINT[5]. 
The decision tree is built from the very small training set. In this 
table each row corresponds to a patient record. We will refer to a 
row as a data instance. The data set contains three predictor 
attributes, namely Age, Gender, Intensity of symptoms and one 
goal attribute, namely disease whose values (to be predicted from 
symptoms) indicates whether the corresponding patient have a 
certain disease or not. 
 
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