International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 5 Issue: 7 633 – 636 _______________________________________________________________________________________________ 633 IJRITCC | July 2017, Available @ http://www.ijritcc.org _______________________________________________________________________________________ Data Mining and Life Science: A Survey Dipti N. Punjani (Main Author) Assistant Professor National Computer College Jamnagar diptipunjani@gmail.com Dr. Kishor Atkotiya (Corresponding Author:) Professor Department of Statistics Saurashtra University- Rajkot atkishor@yahoo.co.in Abstract:- As we are into the age of digital information, the problem of data overload emerges so worryingly ahead. Our ability to analyze and understand immense datasets wrap extreme behind our ability together and stores the data. But a new age group of computational techniques and tools is required to support the extraction of useful knowledge from the rapidly increasing volumes of data. These techniques and tools are the focus of emerging fields of Knowledge Discovery in Databases (KDD) and also called data mining. Data mining is highly noticeable in the fields like marketing, e-commerce or e-business or the fame of its use in KDD in other sectors or industries also. Among these sectors that are just discovering data mining are the fields of medicine and public health also. This research paper provides a survey of current technique of data mining/KDD for healthcare. Keyword: Data Mining, Knowledge Discovery Database __________________________________________________*****_________________________________________________ I. Introduction The purpose of data mining is to extract useful information from large databases or data warehouses. Data mining applications are used for different types of commercial and scientific surface (1). Scientific data mining differentiate itself in the sense that the nature of the datasets is often very different from traditional market driven data mining applications (2). Currently, different data mining algorithms applied in healthcare sector play a significant role in prediction and also diagnosis of different diseases. There are a different number of data mining techniques are establish in the medical related areas like Medical device industry, Pharmaceutical Industry and also Hospital Management. The data generated by the health sector is very vast and complex due to which it is difficult to analyze the data in order to make important decision regarding patient health. This data contains details regarding hospitals, patients, medical claims, treatment costs etc. So, there is a need to generate a powerful tool for analyzing and extracting important information from this complex data. The examination or analysis of health data improves the healthcare by enhancing the performance of patient management tasks. The outcome of data mining technologies are provide different number of benefits of healthcare organizations like grouping the patients having similar type of diseases or health issues so that healthcare organization provides them effective treatments. It can also useful for predicting the number of days to stay of patients in hospital, for medical diagnosis and making plan for effective information system management. Modern technologies are used in medical field to advance the medical services in cost effective manner. Data mining techniques are also used to scrutinize the various factors that are responsible for diseases for example types of foods, different working environment, education level, living conditions, availability of health care services, culture environmental and also agricultural factors (3). Fig.1 Responsible Factors for Disease(4) Medical data are characterized by their heterogeneity with respect to data type. These data may be noisy with erroneous or missing values. The records of millions of patients can be stored and computerized. However, there are other important issues such as ownership