Heart Attack Anxiety Disorder using Machine Learning and Artificial Neural Networks (ANN) Approaches
Naveen Chakravarthy Sattaru, Mohammed Rashad Baker, Dhananjay Umrao, Umesh Kumar Pandey, Mohit Tiwari, M. Kalyan Chakravarthi
Abstract
Heart attack and anxiety disorder are proving to be two major medical complexities in the present scenario. This study illustrates the role of machine learning and artificial neural networks in making successful predictions of these two medical abnormalities. This research paper presents the significance of incorporating artificial intelligence-based mechanisms in minimising the errors of the diagnosis process. In this context, a brief description of the evolution of the usage of AI technology in medical diagnosis has been portrayed here. The different branches of AI like machine learning, artificial neural networks and their contribution to the analysis and interpretation of heart diseases are elaborated in this research paper. Besides primary research has been conducted for the purpose of gaining knowledge about what is the human perception of this technology and its relevance in the modern healthcare system. Analysis and interpretation have also been provided in this research paper to present a clear description of experimental result. Findings suggested that “Random Forest” and Vector Machine are the most used algorithms in heart attack risk prediction and ANN is the least used algorithm. However, developing ANN by integrating autoencoder and feature classifier can perform better in anxiety disorder heart attack prediction.