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A Machine Learning-Based Predictor of Cardiovascular Disease
Subject area: Science,Engineering and Technology · Area of research: Computer Science - Machine Learning
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)
References
[1] Palaniappan, S., & Awang, R. (2008). Intelligent heart disease prediction system using data mining techniques. 2008 IEEE/ACS International Conference on Computer
[2] Dutta, A., Batabyal, T., Basu, M., & Acton, S. T. (2020). An efficient convolutional neural network for coronary heart disease prediction. Expert Systems with Applications, 159, 113408. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
[3] Komal Kumar Napa, G.Sarika Sindhu, D.Krishna Prashanthi, A.Shaeen Sulthana, “Analysis and Prediction of Cardio Vascular Disease using Machine Learning Classifiers”, https://www.researchgate.net/publication /340885231_Analysis_and_Prediction_of _Cardio_Vascular_Disease_using_Machine_ Learning_Classifiers, April 2020.
[4] Muhammad Usama Riaz, SHAHID MEHMOOD AWAN, ABDUL GHAFFAR KHAN, “PREDICTION OF HEART DISEASE USING ARTIFICIAL NEURAL NETWORK”, October 2018
[5] DataSet - https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset
How to cite this paper
@article{1704560,
author = {Rajesh Raavi, Dodda Naveen, N. Revanth Kumar, Sapna R},
title = {A Machine Learning-Based Predictor of Cardiovascular Disease},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {11},
pages = {768-770},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704560.pdf},
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)},
month = {May},
}