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IJSTR >> Volume 9 - Issue 3, March 2020 Edition

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

Website: http://www.ijstr.org

ISSN 2277-8616

A Computational Model For Prediction Of Heart Disease Based On Logistic Regression With Gridsearchcv

[Full Text]



Asvinth A, Manjunatha HIremath



Iterative-Dichotomiser3, CART, Naïve-Bayes, Logistic regression, GridsearchCV.



Heart disease has become a major health issue among various people, irrespective of their age, region, work culture and so on. According to World Health Organisation (WHO), there are 17.9 million people dying every year due to heart related issues. For finding what all are the reasons behind this, requires huge effort and practise especially from doctors and other medical practitioners. Acquiring the medical data is a tedious task, which involves systematic process to get useful insights from it. The objective of this research work is to develop a learning model prototype, which predicts heart disease more accurately. To build proposed model the heart disease dataset (HDD) from the UCI repository is used. The dataset has fourteen (14) attributes including the target class. The proposed research work is implemented with three (3) types of algorithms namely, decision-tree(used both criteria’s gini and entropy), naïve-bayes and logistic regression. Among these three (3) methods Logistic regression gives the highest accuracy of 93%. The model is implemented using python programming language. The implementation details also include the parameter tuning method called GridsearchCV. This model can be improved by considering better learning methods in future.



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