A Survey on Naïve Bayes Algorithm for Diabetes Data Set Problems
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 12
Abstract
Diabetes Mellitus is one of the growing vitally fatal diseases world-wide. A design of classifier for the detection of Diabetes Mellitus with optimal cost and precise performance is the need of the age. The current project implementation looks further to train self-organizing weka effectively classify a diabetic patient as such. weka are so chosen due to their dynamic nature of learning and future application of knowledge. The proposed method here uses a weka implementation of the Naïve Bayes algorithm for designing of classifier. 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.
Authors and Affiliations
Nilesh Jagdish Vispute, Dinesh Kumar Sahu, Anil Rajput
Keywords
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