Experimental evaluation of artificial intelligence assisted heart disease prediction using deep learning principle

Abstract

The system for detecting cardiac illness utilizing Artificial Intelligence (AI) and deep learning algorithms is the main topic of the study. Researchers demonstrate how artificial intelligence can be used to forecast if someone would get cardiac disease. The goal of this work is to create an artificial intelligence system that can detect cardiac problems using machine learning. Diagnose, risk stratification, and management are essentially some of the essential thinking-intensive elements of healthcare that have been automated due to the creation of Artificial Intelligence along with information science, reducing the burden on doctors and lowering the risk of human error. Clinical decision-support platforms frequently employ artificial intelligence approaches for accurate disease prediction and diagnosis. Considering the health history of the individual, we developed a system to determine if a heart disease diagnosis is likely or not for the patient. In order to demonstrate the effectiveness of the suggested strategy, this research established a revolutionary deep learning-based cardiovascular disease diagnosis logic called Efficient Learning based Health Evaluator (ELHE). It is cross-validated with the traditional deep learning model known as Artificial Neural Network (ANN). Many people with cardiovascular disease have the same blatant warning signs that may be used to make a diagnosis. A scheme of detection built around these risk indicators would benefit healthcare providers as well as patients by alerting them to the potential for heart disease before they enter a hospital or undergo pricey diagnostic tests. Finally, the popular learning-oriented tool Python is used to create the recommended prediction logic known as ELHE, which allows the user to input clinical information and understand the present state of a patient's health. This strategy improves healthcare while lowering the cost of treatment. For the medical professional, this technology will serve as a promising tool for accurate diagnosis.

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