Artificial Intelligent Systems and Machine Learning

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Modified Fuzzy Hyper-Line Segment Neural Network and it's Application to Heart Disease Detection


Affiliations
1 Vishwakarma Institute of Technology, Pune, Maharashtra, India
2 Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
     

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In this paper fuzzy hyper line segment neural network (FHLSNN) is modified to represent the learned knowledge in a compact manner using a short hypothesis. This modification has increased percentage recognition rate and reduced recall time per pattern. We have also redefined the membership function so that it suit for the suggested modification. The performance of FHLSNN and modified fuzzy hyper line segment neural network (MFHLSNN) is tested and compared with the two splits of Heart disease database collected from UCI repository. The results shows that MFHLSNN algorithm is found superior to FHLSNN with respect to the recall time per pattern and space requirements for storing the hyper lines.

Keywords

Fuzzy Neural Networks, Heart Disease Detection, Medical Diagnosis, Pattern Recognition.
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  • Modified Fuzzy Hyper-Line Segment Neural Network and it's Application to Heart Disease Detection

Abstract Views: 214  |  PDF Views: 5

Authors

P. S. Dhabe
Vishwakarma Institute of Technology, Pune, Maharashtra, India
A. S. Natu
Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
A. S. Naval
Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
P. H. Parmar
Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
A. M. Padwal
Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
M. L. Dhore
Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India

Abstract


In this paper fuzzy hyper line segment neural network (FHLSNN) is modified to represent the learned knowledge in a compact manner using a short hypothesis. This modification has increased percentage recognition rate and reduced recall time per pattern. We have also redefined the membership function so that it suit for the suggested modification. The performance of FHLSNN and modified fuzzy hyper line segment neural network (MFHLSNN) is tested and compared with the two splits of Heart disease database collected from UCI repository. The results shows that MFHLSNN algorithm is found superior to FHLSNN with respect to the recall time per pattern and space requirements for storing the hyper lines.

Keywords


Fuzzy Neural Networks, Heart Disease Detection, Medical Diagnosis, Pattern Recognition.