Prediction of Diabetes Diagnosis patient Using Classification Based Data Mining Techniques

data mining

Data mining is a process of extracting information from a dataset and transform it into understandable structure for further use, also it discovers patterns in large data sets . Data mining has number of important techniques such as preprocessing, classification. Classification is one such technique which is based on supervised learning.. diabetic is a life threatening disease prevalent in several developed as well as developing countries like India. the data classification is diabetic patients data set is developed by collecting data from hospital repository consists of 1865 instances with different attributes. The instances in the dataset are two categories of blood tests, urine tests. In this paper we discuss various algorithm approaches of data mining that have been utilized for diabetic disease prediction. Data mining is a well known technique used by health organizations for classification of diseases such as diabetes and cancer in bioinformatics research. In the proposed approach we have used WEKA with 10 cross validation to evaluate data and compare results. Weka has an extensive collection of different machine learning and data mining algorithms.

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