首页|Early Detection of Heart Disease Using Feature Selection and Classification Techniques
Early Detection of Heart Disease Using Feature Selection and Classification Techniques
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NETL
Cardiovascular diseases have been recognized as one of the major causes of death in humans。 Majority of the time， the increase in death rate is due to the delay in detecting heart disease。 Early detection would help to save more lives。 Since the early detection of heart disease considers many features and a large volume of data， machine learning techniques can significantly predict heart diseases in the early stages。 In this work， three major feature selection techniques have been deployed before each classifier to acquire better performance and accuracy。 The dataset has been thoroughly examined， processed and the subset of traits that have a significant role in the prediction of heart disease has been extracted。 The classification methods used to classify the retrieved features aided in improving accuracy。
NETL
