Identification of Heart Disease Using Fuzzy Neural Genetic Algorithm with Data Mining Techniques

Identification of Heart Disease Using Fuzzy Neural Genetic Algorithm with Data Mining Techniques

Srikanth MedaRaveendra Babu Bhogapathi

Research scholar, Acharya Nagarjuna University, Guntur & Associate Professor in the Department of Computer Science and Engineering at R.V.R. & J.C. College of Engineering, Guntur 522019, India

Professor, Department of Computer Science and Engineering, R.V.R & J.C College of Engineering, Guntur 552019, India

Corresponding Author Email:
12 April 2018
| |
20 June 2018
| | Citation



Continuous coordinated efforts between Heart specialists and researchers are taking a step ahead for utilization of information mining strategies to the field of individual patient health determination based on clinical records. In this unique situation, this proposed work introduces the utilization of instance acknowledgment and information mining procedures into identification of heart diseases. The information is demonstrated and ordered by utilizing information mining procedures with some constraints in both supervised and unsupervised learning strategies. These constraints are overcome in this examination by utilization of Fuzzy neural system models which can certainly recognize complex nonlinear connections amongst needy and open factors and the capacity to distinguish every single conceivable cooperation between indicator factors. The proposed work recommends that a selection sensitively supports system for Heart solution that can be assembled using the proposed disease prediction models and characterization methods and can be stretched out for other medicinal spaces. A specific concentration is the utilization of Fuzzy Neural Genetic Algorithm (FNGA) and the utilization of mining techniques to create a novel prescient model for use in the Heart Disease Prediction area.


data mining, heart information, fuzzy neural systems, bunching, risk prediction

1. Introduction
2. Related Work
3. Proposed Method
4. Rough Set Approach Using Fuzzy Technique
5. Preprocessing Using Fuzzy Method
6. Experimental Results
7. Conclusion

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