330 Int. J. Biomedical Engineering and Technology, Vol. 17, No. 4, 2015 Copyright © 2015 Inderscience Enterprises Ltd. Machine learning capabilities in medical diagnosis applications: computational results for hepatitis disease Joydip Dhar* and Ashok Ranganathan ABV-Indian Institute of Information Technology and Management, Gwalior 474015, Madhya Pradesh, India Email: jdhar@iiitm.ac.in Email: ashok.iiitm@gmail.com *Corresponding author Abstract: The main goal of the research work is to apply a Genetic Algorithm (GA) in order to prune the inputs for an Artificial Neural Network (ANN) for medical diagnosis in order to reduce the computational complexity. The inputs in medical diagnosis are the diagnostic factors. The GA implemented creates the essential and minimal subset of diagnostic factors required for medical diagnosis. Firstly, the ANN is applied alone and the time taken and efficiency of the medical diagnostic system are recorded. Then, pruning of inputs using GA and then the pruned inputs are used for the ANN, and the time taken and efficiency obtained are compared with the previous one. The medical diagnostic data set is taken from UCI medical repository for the hepatitis disease. There is a significant percentage reduction in training time as well as testing time of ANN and a significant improvement in the success rate of diagnosis. Keywords: machine learning; genetic algorithm; ANN optimisation; artificial neural network; medical diagnosis. Reference to this paper should be made as follows: Dhar, J. and Ranganathan, A. (2015) ‘Machine learning capabilities in medical diagnosis applications: computational results for hepatitis disease’, Int. J. Biomedical Engineering and Technology, Vol. 17, No. 4, pp.330–340. Biographical notes: Joydip Dhar is currently working as an Associate Professor at ABV-IIITM, Gwalior. He completed his PhD from IIT, Kanpur, in 1997. He has more than 17 years of teaching and research experience. He has delivered more than 50 invited lectures at different universities and institutions in India and abroad (UK). He has co-authored a research book published by Lambert Academic Publishing. He has completed three sponsored projects and two are currently undergoing. His research interests are computational mathematics, mathematical modelling and simulation of biological, environmental, managerial and engineering systems. Ashok Ranganathan is currently working at Samsung as a Software Developer, since June 2013. He completed MTech in Information Technology from ABV- Indian Institute of Information Technology and Management, Gwalior, India, in 2013. His research interests are information technology, cording, system modelling and soft computing.