Comparison between ANN-Based Heart Stroke Classifiers Using Varied Folds Data Set Cross-Validation

  • H. S. Niranjana Murthy
  • M. Meenakshi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 308)


This paper presents the design and development of an artificial neural network (ANN) model for the classification of heart stroke disease. The novelty of this work is training multilayer perceptron (MLP) neural network architectures with back-propagation algorithm for multivariate large data sets. Subsequently, the performances of the ANN models are evaluated using database of heart stroke obtained from Cleveland Clinic Foundation Database with all attributes are numeric-valued. The accuracy of the designed ANN models are cross-validated using varied folds of data set comprising 303 instances extracted from different age groups. This study exhibits ANN-based prognosis for early detection of level of heart stroke with testing classification accuracy of 85.55 %.


Artificial neural network Multilayer perceptron Coronary heart disease 



Our thanks to the experts who have contributed in development of Cleveland Heart Disease database.


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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.M.S. Ramaiah Institute of TechnologyBengaluruIndia
  2. 2.Dr. Ambedkar Institute of TechnologyBengaluruIndia

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