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IJSTR >> Volume 8 - Issue 8, August 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Implementation Of An Efficient Hybrid Classification Model For Heart Disease Prediction

[Full Text]

 

AUTHOR(S)

Manjari Agarwal, Dr. Gaurav Kumar Ameta

 

KEYWORDS

Classification Techniques, Data Mining, Heart Disease Prediction, k-Nearest Neighbor (k-NN), Prediction Analysis, Support Vector Machine (SVM), Supervised Machine Learning.

 

ABSTRACT

The prediction analysis is applied for predicting future possibilities based on the current information. Prediction for future possibilities has been made feasible following three major steps named as pre-processing, feature extraction and classification, in today’s perspective. My research exhibits the work in two stages to increase the accuracy of prediction regarding cardiac issues. Whereas in the first stage, a novel method was proposed in which factors like pulse rate, cholesterol etc. are included along with the age of patient as compared to the previous research study in which only age was taken as a primary attribute for prediction. The primitive attributes are changed in the proposed study for better predictions to receive facts as compared to the older technique. In stage second, a new and efficient hybrid classification model was designed, which is the combination of two different classification methods i.e. support vector machine and k-Nearest Neighbour. The support vector machine (SVM) will extract the features of the dataset and k-Nearest Neighbour classifier will generate the final classified result. The performance of the proposed model in terms of accuracy and execution time is higher as compared to existing method.

 

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