Abstract

Machine Learning Techniques and Syntactic Pattern Recognition based Heart Disease Prediction for Smart Health

Cardiovascular Disease (CVD) may sometimes unexpected loss of life. It affects the heart and blood vessels of the body. CVD plays an important factor in life since it may cause the death of a human. It is necessary to detect early of this disease for secure a patient’s life. In this chapter two exclusively different methods are proposed for the detection of heart disease. The first one is Pattern Recognition Approach with grammatical concepts and the second one is the machine learning approach. In the syntactic pattern recognition approach initially, ECG wave from different leads is decomposed into pattern primitive based on diagnostic criteria. These primitives are then used as terminals of the proposed grammar. Pattern primitives are then input into the grammar. The parsing table is created in a tabular form. It finally indicates the patient with any disease or normal. Here five diseases besides normal are considered. Different Machine Learning (ML) approaches may be used for detecting patients with CVD and assisting health care systems also. These are useful for learning and utilizing the patterns discovered from large databases. It applies to a set of information to recognize underlying relationship patterns from the information set. It is a learning stage. Unknown incoming set of patterns can be tested using these methods. Due to its self-adaptive structure, Deep Learning (DL) can process information with minimal processing time. DL exemplifies the use of the neural network. A predictive model follows DL techniques for analyzing and assessing patients with heart disease. A hybrid approach based on Convolutional Layer and Gated-Recurrent Unit (GRU) is used in the paper for diagnosing heart disease.

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