Effective Prediction of Cardiovascular Disease Using Deep Learning

Today's leading cause of death worldwide is cardiovascular disease, which has risen to the top of the list of diseases in terms of diagnostic difficulty. Cardiovascular disease is more likely to occur in a person with chest pain, depression, hypertension, smoking, women with early menopause, diabetes, high cholesterol, and over drinking. Early prediction of cardiovascular disease is needed to save more lives. Here comes the saviour Machine Learning algorithms that are less expensive with more accuracy. Some of the common machine learning algorithms are implemented to predict the disease. Different techniques provide different accuracies depending on the attributes, dataset, and tools used for implementation. Using the ECG dataset, we create an 11-layer Convolutional Neural Network 2D in this study. We have proposed two models namely Cardiovascular Disease Detection—Machine Learning (CVD-ML) that can predict Cardiovascular Disease using real-time numerical data and Cardiovascular Disease Detection—Deep Learning (CVD-DL) using the ECG Image. By using ensembling technique, we have attained the highest accuracy of 94.6% for real-time numerical data and by using Convolutional Neural Network we have attained the accuracy of 99.9% for ECG data. Therefore, Artificial Intelligence techniques used are highly reliable and effective in providing accuracy for cardiovascular disease prediction.