Exploration of Healthcare Using Data Mining Techniques

Exploration of Healthcare Using Data Mining Techniques

Anindita Desarkar (Birla Institute of Technology Mesra – Kolkata, India) and Ajanta Das (Birla Institute of Technology Mesra – Lalpur, India)
DOI: 10.4018/978-1-5225-5222-2.ch014
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Huge amount of data is generated from Healthcare transactions where data are complex, voluminous and heterogeneous in nature. This large dataset can be used as an ideal store which can be analyzed for knowledge discovery as well as various future predictions. So, Data mining is becoming increasingly popular as it offers set of innovative tools and techniques to handle this kind of data set whereas traditional methods have limitations for that. In summary, providing the better patient care and reduction in healthcare cost are two major goals of application of data mining in healthcare. Initially, this chapter explores on the various types of eHealth data and its characteristics. Subsequently it explores various domains in healthcare sector and shows how data mining plays a major role in those domains. Finally, it describes few common data mining techniques and their applications in eHealth domain.
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Data mining which is a newer technology, came into picture predominantly only in 1994. After that it was used in almost every domains including healthcare management. Various data mining applications can be created to measure the efficiency of current medical treatments. It can generate a report regarding the most appropriate and effective action on that condition by analyzing the causes, symptoms and course of treatments. As example, the patient’s condition can be compared after applying different drugs for a specific disease to find the most appropriate and cost effective treatment. United Healthcare has implemented it by mining its treatment record data to provide better treatment to its patients in reduced cost (Durairaj & Ranjani, 2013).

Jayanthi Ranjan already presented the discovery and extraction of useful data patterns to find observable patterns from huge volume. It proves the advantages of data mining in pharma industry in decision making process along with other issues like adverse reactions to the drugs (Ranjan, 2007).

K. Srinivas, B. Kavitha Rani and Dr. A. Goverdhan had used classification based data mining techniques on huge data set to predict the likelihood of having heart diseases for a patient. It takes age, sex, blood pressure, blood pressure as inputs from the patient profile (Srinivas, 2010).

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