Classification of biomedical datasets using Master-Slave Synchronisation of Lorenz System

In this study we propose a novel method for discrimination of the attributes of biomedical sensory datasets using Master-Slave Synchronization of chaotic Lorenz Systems. As part of the performance testing, three benchmark biomedical datasets (Vertebral Column dataset, E. Coli dataset and Iris dataset) were presented to our novel algorithm and the output vector were then used as input matrices to three classifier algorithms, namely Artificial Neural Networks (ANN), Decision Tree (DT) and K-Nearest Neighbour (KNN). The performance of the classifiers was then evaluated using the original and pre-processed datasets.