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Published in:
2017 | OriginalPaper | Chapter
Heart Disease Prediction System Using Random Forest
Authors : Yeshvendra K. Singh, Nikhil Sinha, Sanjay K. Singh
Published in: Advances in Computing and Data Sciences
Publisher: Springer Singapore
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Abstract
The scope of Machine Learning algorithms are increasing in predicting various diseases. The nature of machine learning algorithm to think like a human being is making this concept so important and versatile. Here the challenge of increasing the accuracy of Heart disease prediction is taken upon. The non-linear tendency of the Cleveland heart disease dataset was exploited for applying Random Forest to get an accuracy of 85.81%. The method of predicting heart diseases using Random Forest with well-set attributes fetches us more accuracy. Random Forest was built by training 303 instances of data and authentication of accuracy was done using 10-fold cross validation. By the proposed algorithm for heart disease prediction, many lives could be saved in the future.