Author : B.V. Baiju and R.J. Remy Janet

Data Mining is non trivial extraction of implicit data, previously not known, and imaginably useful information from data. Data mining is an essential process where intelligent methods are applied in order to extract data patterns. Using data mining we can evaluate patterns which we can use in future to take intelligent decisions and we can present the knowledge we extracted in better way. Data Mining refers to using a variety of techniques to identify information or decision making knowledge in the database and extracting these in a way that they can put to use in areas such as decision making, predictions, for valuable forecasting and computation. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information, to take decisions effectively, to discover the relations that connect parameters in a database is the subject of data mining. As large amount of data is generated in medical organisations (hospitals, medical centers) but as this data is not properly used. There is a wealth of hidden information present in the datasets. This unused data can be converted into useful data. For this purpose we can use different data mining techniques. In this study, we are applying Naïve Bayes data mining classifier technique which produces an optimal prediction model using minimum training set. Data mining is the analysis step of the Knowledge Discovery in Databases process (KDD). Data mining involves use of techniques to find underlying structures and relationships in a large database. Using medical profile such as age, sex, blood pressure and blood sugar we can easily predict the likelihood of patients getting heart disease. In this paper we have evaluated the performance of new classification approach that uses the experienced Doctor’s knowledge to assign the weight to each attribute. More weight is assigned to the attribute having high impact on disease prediction.

Keywords: Data mining, Naive bayes, heart disease, KDD, disease prediction

Article published in International Journal of Current Engineering and Technology, Vol.5, No.2 (April-2015)