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Hussein Attya Lafta
university of babylon
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Classification of medical datasets using back propagation neural network powered by genetic-based features elector Hussein Attya Lafta; Zainab Falah Hasan; Noor Kadhim Ayoob
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (528.434 KB) | DOI: 10.11591/ijece.v9i2.pp1379-1384

Abstract

The classification is a one of the most indispensable domains in   the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
Co-Authors Noor Kadhim Ayoob Zainab Falah Hasan