Comparison of Different Classification Techniques Using WEKA for Hematological Data

ABSTRAC : Medical professionals need a reliable prediction methodology to diagnose hematological data comments. There are large quantities of information about patients and their medical conditions. Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Weka is a data mining tools. It contains many machine leaning algorithms. It provides the facility to classify our data through various algorithms. Classification is an important data mining technique with broad applications. It classifies data of various kinds. Classification is used in every field of our life. Classification is used to classify each item in a set of data into one of predefined set of classes or groups. In this paper we are studying the various Classification algorithms. The thesis main aims to show the comparison of different classification algorithms using Waikato Environment for Knowledge Analysis or in short, WEKA and find out which algorithm is most suitable for user working on hematological data. To use propose model, new Doctor or patients can predict hematological data Comment also developed a mobile App that can easily diagnosis hematological data comments. The best algorithm based on the hematological data is J48 classifier with an accuracy of 97.16% and the total time taken to build the model is at 0.03 seconds. Naïve Bayes classifier has the lowest average error at 29.71% compared to others.