Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks

Information Technology offer health care industry significant potential to improve productivity and quality of patient care. The area of Data mining in health care is growing rapidly because of strong need for analyzing the vast amount of clinical data bases stored in hospitals. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these volumes of data into useful information for decision making. In this paper ,the potential use of classification based data mining techniques such as Decision tree, Naïve Bayes are applied to massive volume of healthcare data.Experimental results predicts the hearthattack results for efficient decision making purpose.

Association Rules Technique

Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. These tools can include statistical models, mathematical algorithms, and machine learning methods (algorithms that improve their performance automatically through experience, such as neural networks, Association Rules or decision trees). Consequently, data mining consists of more than collecting and managing data, it also includes analysis and prediction. 

Association Rules is an approach that analyzes the characteristics between sets of data in order to find out those similar patterns or relationships that have been highly relied on in merchandising. By analyzing the patterns of the customers’ purchase behaviors, association rules are able to reveal the preferences of products, correlations between each purchase and further predict the consumers’ choices. For example, when one product is often purchased with another one, there is an association. This technique is frequently used by retail stores to assist in areas such as marketing, advertising, product promotion, and product placement.

Components of customer relationship management

Customer relationship management is a combination of several components. Before the process can begin, the firm must first possess customer information. Companies can learn about their customers through internal customer data or they can purchase data from outside sources. There are several sources of internal data:

_ summary tables that describe customers (e.g., billing records)

_ customer surveys of a subset of customers who answer detailed questions

_ behavioral data contained in transactions systems (web logs, credit card records,

etc).

An enterprise data warehouse is a critical component of a successful CRM strategy. Most firms have massive databases that contain marketing, HR, and financial information. However, the data required for CRM can be limited to a marketing data mart with limited feeds from other corporate systems.

CRM system must analyze the data using statistical tools, OLAP, and data mining. Whether the firm uses traditional statistical techniques or one of the data mining software tools, marketing professionals need to understand the customer data and business imperatives.

The term “customer lifecycle” refers to the stages in the relationship between a customer and a business. It is important to understand customer lifecycle because it relates directly to customer revenue and customer profitability. Marketers say there are three ways to increase a customer’s value: (1) increase their use (or purchases) of products they already have; (2) sell them more or higher-margin products; and (3) keep the customers for a longer period of time.

The customer lifecycle provides a good framework for applying data mining to CRM. On the “input” side of data mining, the customer lifecycle tells what information is available. On the “output” side, the customer lifecycle tells what is likely to be interesting

Telecommunications

Telecommunication companies around the world face escalating competition which is forcing them to aggressively market special pricing programs aimed at retaining existing customers and attracting new ones. Knowledge discovery in telecommunications include the following

Call detail record analysis—Telecommunication companies accumulate detailed call records. By identifying customer segments with similar use patterns, the companies can develop attractive pricing and feature promotions.

_ Customer loyalty—Some customers repeatedly switch providers, or “churn”, to take advantage of attractive incentives by competing companies. The companies can use data mining to identify the characteristics of customers who are likely to remain loyal once they switch, thus enabling the companies to target their spending on customers who will produce the most profit.

 Knowledge discovery applications are emerging in a variety of industries :

 _ Customer segmentation—All industries can take advantage of data mining to discover discrete segments in their customer bases by considering additional variables beyond traditional analysis.

_ Manufacturing—Through choice boards, manufacturers are beginning to customize products for customers; therefore they must be able to predict which features should be bundled to meet customer demand.

_ Warranties—Manufacturers need to predict the number of customers who will submit warranty claims and the average cost of those claims.

_ Frequent flier incentives—Airlines can identify groups of customers that can be given incentives to fly more.

Customer Relationship Management is defined by four elements of a simple framework:

 CRM requires the firm to know and understand its markets and customers. This involves detailed customer intelligence in order  to select the most profitable customers and identify those no longer worth targeting.

CRM also entails development of the offer: which products to sell to which customers and through which channel. In selling, firms use campaign management to increase the marketing department’s effectiveness. Finally, CRM seeks to retain its customers through services such as call centers and help desks.

CRM is essentially a two-stage concept. The task of the first stage is to master the basics of building customer focus. This means moving from a product orientation to a customer orientation and defining market strategy from outside-in and not from inside-out. The focus should be on customer needs rather than product features. Companies in the second stage are moving beyond the basics; they do not rest on their laurels but push their development of customer orientation by integrating CRM across the entire customer experience chain, by leveraging technology to achieve real-time customer management, and by constantly innovating their value proposition to customers

e-commerce

With the rapid expansion of e-commerce, more and more products are sold on the Web, and more and more people are also buying products online. In order to enhance customer satisfaction and shopping experience, it has become a common practice for online merchants to enable their customers to review or to express opinions on the products that they have purchased. With more and more common users becoming comfortable with the Web, an increasing number of people are writing reviews. As a result, the number of reviews that a product receives grows rapidly. Some popular products can get hundreds of reviews at some large merchant sites. Furthermore, many reviews are long and have only a few sentences containing opinions on the product. This makes it hard for a potential customer to read them to make an informed decision on whether to purchase the product. If he/she only reads a few reviews, he/she may get a biased view. The large number of reviews also makes it hard for product manufacturers to keep track of customer opinions of their products. For a product manufacturer, there are additional difficulties because many merchant sites may sell its products, and the manufacturer may (almost always) produce many kinds of products.

security

Existing work on security-enhanced data transmission includes the designs of cryptography algorithms and system infrastructures and security-enhanced routing methods. Their common objectives are often to defeat various threats over the Internet, including eavesdropping, spoofing, session hijacking, etc. Among many well-known designs for cryptography based systems, the IP Security (IPSec) and the Secure Socket Layer (SSL) are popularly supported and implemented in many systems and platforms. Although IPSec and SSL do greatly improve the security level for data transmission, they unavoidably introduce substantial overheads, especially on gateway/host performance and effective network bandwidth. For example, the data transmission overhead is 5 cycles/byte over an Intel Pentium II with the Linux IP stack alone, and the overhead increases to 58 cycles/byte when Advanced Encryption Standard (AES) is adopted for encryption/decryption for IPSec.

The Man...

A man always lives for himself,
yet claims to be the harbinger of a peaceful society
Off the shelf of comforts from the outside of the door of unseeing eyes,
Living in luxury with in the concealed thoughts !

Wearing a rope of trust across to hold the gown called Love
dipped in the sweat of toiled hearts !
Greedy eyes with false tears drawn at will from the
well of non-existent well of Pity...
Name the soul that has ever witnessed the truth behind those empty eyes !

Wake up from the sleep oh my soul !
My dear blood that holds the heart and mind together,
Find your true self, embrace the beauty of suffering...
Find the answers in the desolate desperation to Change !