Prediction and diagnosis of cardiovascular disease using cloud and machine learning design

Abstract

Predicting and accurately identifying heart disease is a significant challenge in the field of medicine, and the problem of cardiovascular disease predetermine in the health care system is regarded as an essential challenge. Patients have access to more expensive surgical procedures at these rapidly expanding health care organisations. Recent years have seen an increase in the prevalence of heart disease; this means that despite the progress that has been made in medicine, the prevalence of cardiovascular disease continues to rise at an alarming rate. The primary contributors to the development of these illnesses are a sedentary lifestyle, excessive use of alcohol, insufficient time spent being physically active, and the use of cigarette products. As a result, there is a requirement for a cloud-based framework (CBF) that is capable of monitoring health information and making accurate predictions regarding it. Recently, techniques from the field of machine learning have been applied in an effort to address issues of this nature. But the method that is being suggested uses a cloud-based and cloud-based four-step process to improve surveillance of patients’ health information. This is done to improve the process of forecasting patients’ health information. Detecting and categorising cardiac illness can be accomplished through the application of two distinct kinds of machine learning techniques. After that, an analysis is performed to determine how accurate those techniques are. In order to assess how effectively they work, evaluation parameters are utilised.