A Machine Learning-Based Predictor of Cardiovascular Disease

• Author(s): Rajesh Raavi ; Dodda Naveen ; N. Revanth Kumar ; Sapna R
• Paper ID: 1704560
• Page: 768-770
• Published Date: 30-05-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 11 May-2023

Abstract

Heart disease is the biggest cause of death worldwide. Various forms of cutting-edge technologies are used to treat heart conditions. The fact that many medical staff members lack the skills necessary to treat patients successfully is the most common issue in healthcare facilities. They consequently form their own opinions, which frequently result in disastrous effects. Early illness identification is essential since the prevalence of heart disease is rising at an alarming rate. The study's main goal is to identify which people, depending on a range of medical indications, are most prone to acquire heart disease. To predict and identify persons with heart disease, we used a variety of techniques, such as logistic regression, random forests, and k-nearest neighbour (KNN) algorithms. The suggested method can predict a person's risk of acquiring cardiovascular disease with accuracy. This technique for predicting heart illness enhances patient care, facilitates the diagnosis of the condition, and permits the contemporaneous exploration of massive amounts of data.

Keywords

K-nearest neighbour (KNN), Logistic Regression (LR), Cardiovascular Disease (CVD), Heart Disease Prediction System (HDPS)

Citations

IRE Journals:
Rajesh Raavi , Dodda Naveen , N. Revanth Kumar , Sapna R "A Machine Learning-Based Predictor of Cardiovascular Disease" Iconic Research And Engineering Journals Volume 6 Issue 11 2023 Page 768-770

IEEE:
Rajesh Raavi , Dodda Naveen , N. Revanth Kumar , Sapna R "A Machine Learning-Based Predictor of Cardiovascular Disease" Iconic Research And Engineering Journals, 6(11)