Non Linear Cellular Automata in Predicting Heart Attack

Non Linear Cellular Automata (NLCA) as a modeling tool has received a considerable attention in recent years. Researchers from different fields have proposed various cellular automata models for addressing several problems in bioinformatics, image processing and network security .In this paper we investigate the computational properties of Non Linear Cellular Automata for building versatile and robust CA data based modeling tool for predicting heart attack. In this paper, we investigate the non linear classes of Cellular Automata for predicting heart attack. We are mostly interested in computational properties of Non Linear Cellular Automata with decidable features and regularity. We also propose the framework of special class of non linear cellular automata named as Non Linear Multiple Attractor Cellular Automata (NLMACA). This framework is supported with genetic evolution to arrive at the desired local rules of a non linear cellular automata global function. The performances of the proposed classifier were evaluated in terms of training performances and classification accuracies and the results showed that the proposed classifier has good potential in predicting the heart attack.

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