A Computationally Effective Remote Health Monitoring Framework using AGTO-MLRC Models for CVD Diagnosis

• Menda Ebraheem,
• Aravind Kumar Kondaji,
• Y Butchi Raju,
• Dr N Bhupesh Kumar,

Vol. 18, No. 9, September 30, 2024
10.3837/tiis.2024.09.004, Download Paper (Free):

• Cardiovascular Disease (CVD)
• Remote Healthcare
• Internet of Things (IoT)
• Artificial Gorilla Troop Optimization (AGTO)
• and Multi-Linear Regression Classification (MLRC).

Abstract

One of the biggest challenges for the medical professionals is spotting cardiovascular issues in the earliest stages. Around the world, Cardiovascular Diseases (CVD) are a major cause of death for almost 18 million people each year. Heart disease is therefore a serious concern that needs to be treated. The numerous elements that affect health, such as excessive blood pressure, elevated cholesterol, aberrant pulse rate, and many other factors, might make it challenging to detect heart disease. Consequently, early disease detection and the development of effective treatments can benefit greatly from the field of artificial intelligence. The purpose of this work is to develop a new IoT based healthcare monitoring framework for the prediction of CVD using machine learning algorithm. Here, the data preprocessing has been performed to create the normalized dataset for improving classification. Then, an Artificial Gorilla Troop Optimization (AGTO) algorithm is deployed to choose the most pertinent features from the normalized dataset. Moreover, the Multi-Linear Regression Classification (MLRC) model is also implemented for accurately categorizing the medical information as whether healthy or CVD affected. The results of the proposed AGTO-MLRC mechanism is validated and compared using the popular benchmarking datasets.

Statistics

Cite this article