Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 4, Pages: 1-8
Sivagowry Shathesh* and M. Durairaj
School of Computer Science, Engineering and Applications, Bharathidasan University, Trichy – 620024, Tamil Nadu, India; [email protected], [email protected]
*Author For Correspondence
Sivagowry Shathesh School of Computer Science, Engineering and Applications, Bharathidasan University, Trichy – 620024, Tamil Nadu, India; [email protected]
Background/Objectives: The objective is to generate rules to predict the severity of Cardiac disease by applying Rough Set Theory, Particle Swarm Optimization (PSO) with Artificial Neural Network (ANN) and Fuzzy Logic (FL). Methods/ Statistical Analysis: In order to achieve the aim, the Rough Set Theory is hybrided with PSO to give Intelligent Hybrid Quick Reduct Particle Swarm Optimization (IHQRPSO) Algorithm which explore the search space and find the optimal data. The optimal data set is then classified by using ANN as disease and undiseased patients and finally the rules are framed using FL to predict the severity of disease. Findings: By using the IHQRPSO Algorithm, the attribute number has been reduced from 13 to 5. The optimal data set is then trained by ANN which used Back propagation Learning algorithm for classification. The classified unhealthy patients are then categorized to risk level such as mild, low and severe based on the rules obtained from Fuzzy Inference System. A total of 63 rules is framed. The whole Hybrid mechanism is then compared with the existing work which used Genetic Algorithm and Fuzzy Temporal Rule Mining Algorithm for prediction. While comparing the proposed hybrid mechanism with existing one, the prediction accuracy has been increased to a considerable level. Applications/Improvements: The Performance of the algorithm is evaluated in terms of accuracy. The accuracy was 95.25% while using the proposed algorithm where it was around 85 while using the existing work.
Keywords: Cardio Vascular Disease, Hybrid Mechanism, Intelligent, Prediction, Risk
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