摘要
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.
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.
