Intelligent Healthcare Platform: Cardiovascular Disease Risk Factors Prediction Using Attention Module Based LSTM

Abstract

Cardio-Vascular Disease (CVD) is one of the leading cause of death all over the world with expecting approximately 23.6 million individuals to be attacked by the CVD by 2030. Thus, the healthcare industry is trying to gather a large amount of CVD information, which can help the doctors to detect and identify the potential risk factors of the CVD. Deep learning can dig out the hidden pattern of the disease and symptoms from this structured and unstructured medical information. As a result, in this paper, we propose an algorithm to predict the risk factors of the CVD using the attention module based Long Short- Term Memory (LSTM), which has almost 95% accuracy and 0.90 Matthews Correlation Coefficient (MCC) scores; better than any other previously proposed methods. Moreover, we propose a novel Intelligent Healthcare Platform for continuous data collection and patient monitoring system. Initially, the proposed platform is used for data collection, and we find out the best suitable features from the dataset for applying various machine learning algorithms. The experimental results show that the attention module based LSTM outperforms than the other statistical machine learning algorithms for the prediction as well as indicates significant risk factors of the CVD, which can be supportive for the CVD patients to change their lifestyle.

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