Cardiovascular Disease Prediction using Deep Learning

Abstract

One of the most important structures in the human body is the heart. It is at the focal point of the circulatory framework. Heart disease is a potentially fatal condition that can result in death or severe long-term impairment. Therefore, efficient techniques for uncovering hidden linkages and there are no clear patterns in e-health data. Medical diagnosis is a difficult task that is crucial to preserving lives, so it must be done correctly and quickly. To lower the cost of performing clinical tests, a suitable and precise computer-based automated decision support system is necessary. The use of machine learning in health analytics has been presented as a way to anticipate reliable patient data analysis. The information generated by the healthcare industry is not mined. In the medical industry, data mining techniques can be utilized to create an intelligent model employing data sets that include patient risk factors. The emergence of ideas and techniques for making use of data has surprised knowledge discovery in databases (KDD). This study delves into the usage of deep learning and machine learning approaches in disease diagnosis. In recent years, many data mining classifiers have been created to aid in the accurate and timely identification of illnesses. This study offers a heart attack forecasting model based on deep learning methodologies, notably Multi-Layer Perceptron (MLP), to anticipate a patient's likelihood of acquiring heart disease. MLP is an advanced type approach that employs the Artificial Neural Network's Deep Learning approach. Deep learning and data mining are used in the suggested approach to get reliable and error-free outcomes.

Topics & Concepts