Performance comparison of Machine Learning Algorithms for diagnosis of Cardiotocograms with class inequality

The objective of the present paper is to demonstrate the potential of Computational Intelligence in applications pertaining to the automatic identification - categorisation of Cardiotocograms using Machine Learning Algorithms and Artificial Neural Networks whose purpose is to distinguish between healthy or pathological cases leading to mortality during birth or fetal cerebral palsy. Interest is also placed on the performance of the Machine learning algorithms and the comparison of the classifiers´ results.