Smart Predictive Models for Enhancing Cardiac Health Outcomes Using Deep Learning Techniques

Authors -Mr. R. V. Viswanathan, R. Siva Harish, K. S. Rajesh, R. Dhanush

Abstract- – In The heart is one of the most important parts of the human body because it is the system’s nerve center. Heart disease is one of the most dangerous and life-threatening diseases that can lead to death or a disabling condition for the rest of a person’s life. However, there are not many effective ways to discover the hidden trends and relationships in the e-health data. This is because medical diagnosis is a critical process that has to be done correctly in order to save lives. To reduce the overall cost of performing the clinical tests, it is crucial to develop and implement a suitable and accurate computer-based automated decision support system. The use of health analytics in an attempt to perform proper analysis of patient data has been proposed. The healthcare industry data is being examined. The medical sector is able to develop smart models by sets of patient risk factors using data mining techniques. The development of the use of data has been a surprise to Knowledge Discovery in Databases (KDD). This project provides a glimpse of the Machine Learning and Deep Learning approaches that are used in the diagnosis of diseases. There are many data mining classifiers that have been discussed in the last year for quick and accurate illness diagnosis. The heart disease prediction system proposed in this project uses deep learning techniques, more especially Multi-Layer Perceptron (MLP), to predict the likelihood of the patient developing heart-related complications. MLP, a very efficient classification method, employs the Deep Learning technique from Artificial Neural Networks. The proposed model returns accurate results with minimum error by combining deep learning and data mining.

DOI: /10.61463/ijset.vol.11.issue2.387