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Cardiovascular disease or CVD is among the leading causes of death around the globe in which the blood vessels of the heart that supply myocardium gets affected causing different heart related diseases like including vascular, ischemic, and hypertensive. In today’s typical modern society, fatalities from cardiovascular disease have become one of the biggest challenges with around one person dying from heart disease every minute. However, identifying and predicting the onset of disease at early stages is a difficult task. Therefore, an effective deep learning-based Bi-LSTM approach is proposed in this paper for detecting CVDs. The main objective of the proposed approach is to reduce the complexity and to increase the classification accuracy of the system. To accomplish this task, information is collected from the dataset available on UCI ML repository. This data is then processed and normalized so that unnecessary and redundant data is removed from it. Moreover, in order to make the dataset more informative and useful, an Infinite Feature selection technique is implemented wherein only the critical and useful features are selected from the available set of features. For classifying the data, a bidirectional LSTM (Bi-LSTM) classifier is used. The effectiveness of the suggested approach is validated in the MATLAB software and later on compared with traditional CNN, KNN and NB models in terms of various dependency factors that demonstrate its efficacy.
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