Published Paper Details:

Cardiovascular diseases are one of the leading causes of death worldwide, killing 17.9 million people annually. Coronary heart disease, cerebrovascular disease, rheumatic heart disease and other diseases are one of the categories of cardiovascular diseases known as cardiovascular diseases. More than four out of five deaths from myocardial infarction are related to heart attack and stroke, with one-third occurring before the age of 70. Unhealthy diet, lack of exercise, use of tobacco products and alcohol consumption are the main behavioral risk factors for the heart. Disease and stroke People may experience high blood pressure, high blood sugar levels, high blood lipids, and even overweight or obesity as a result of behavioral risk factors. Approximately 90 percent of cardiovascular disease is preventable if predicted in advance, providing clinicians with important insights that allow them to tailor diagnosis and treatment on a patient-by-patient basis. Machine learning techniques are one of the most successful techniques for forecasting. Various studies have only mentioned the use of ML systems to predict heart disease. One of the biggest challenges today is predicting disease and its causes. This study presents a new method for identifying large datasets using deep learning algorithms to improve the accuracy of cardiovascular disease prediction. This model used artificial neural networks and feature selection to predict cardiovascular diseases. ANN is an ML technique that can be used to produce useful predictive results and feature selection to select the most appropriate features from a dataset.