Author(s)

Fahmida Akter¹, Md Altab Hossin², Golam Moktader Daiyan³, Md. Motaher Hossain⁴

¹Department of Computer Science and Engineering, Coxbazar International University, Chittagong, Bangladesh.
²Department of Information Management and Ecommerce, University of Electronic Science and Technology of China, Chengdu, China.
³Department of Computer Science and Engineering, East Delta University, Chittagong, Bangladesh.
⁴Department of Computer Science and Engineering, University of Asia Pacific, Dhaka, Bangladesh.

Over the years, the amount of information about patients and medical information has grown substantially. Moreover, due to an increase of blood diseases patients, conventional diagnostic tests have been using by the medical pathologists which are low in cost and result in an inaccurate diagnosis. To recognize optimal disease pattern from hematological data, a reliable prediction methodology is needed for medical professionals. Data mining approaches permit users to examine data from various dimensions, group it and sum up the relationships identified. Classification is a vital data mining technique with extensive applications. Classification algorithms are applied to categorize every item in a set of data into one of a known set of classes. The objective of this paper is to compare different classification algorithms using Waikato Environment for Knowledge Analysis and to find out a most effective algorithm for end-user functioning on hematological data. The most efficient algorithm found is Random Forest having accurateness at 96.47% and the overall time is taken to construct the model is 0.16 seconds which is more efficient than different existing works. On the contrary, Multilayer Perceptron classifier has the lowest accuracy of 75.29% with 1.92 seconds to construct the model.

Data Mining, Random Forest, Multilayer Perception, Bayesian Network

Akter, F. , Hossin, M. , Daiyan, G. and Hossain, M. (2018) Classification of Hematological Data Using Data Mining Technique to Predict Diseases. Journal of Computer and Communications, 6, 76-83. doi: 10.4236/jcc.2018.64007.