Authors :
Maranani Pavan Kumar; Kanuboyina Sai Venkat Teja; Mallidi Chinna Rama Chandra Reddy; P. Srinu Vasa Rao
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/4unxj2fw
Scribd :
https://tinyurl.com/2p8xhd53
DOI :
https://doi.org/10.5281/zenodo.10793056
Abstract :
Because heart disease is common in humans,
efforts are being made to improve the treatment and
diagnosis of heart disease. As technology and clinical
analyzes become more collaborative, data discovery and
clinical data can improve patient management.
Diagnosis and diagnosis of heart disease is an
important medical task to ensure classification and thus
help cardiologists provide appropriate treatment to
patients. The use of machine learning in medicine is
increasing because they can identify patterns in data.
Using machine learning to predict heart disease could
help doctors reduce risk. This study aims to analyze
various aspects of patient data to provide accurate
predictions of heart disease. Based on our analysis, the
most important predictors of cardiovascular disease
were identified using the most selective correlation
methods and best-in-class studies. Studies have found
that the most important factors in the diagnosis of heart
disease are age, gender, smoking, obesity, diet, physical
activity, stress, type of chest pain, previous chest pain,
diastolic blood pressure, diabetes, troponin,
electrocardiogram and targets. This program can be
used as an early prediction of heart disease.
Keywords :
Cardiovascular, Artificial Intelligence, Logistic Regression, Naive Bayes, K Nearest Neighbor, Multilayer Perceptron.
Because heart disease is common in humans,
efforts are being made to improve the treatment and
diagnosis of heart disease. As technology and clinical
analyzes become more collaborative, data discovery and
clinical data can improve patient management.
Diagnosis and diagnosis of heart disease is an
important medical task to ensure classification and thus
help cardiologists provide appropriate treatment to
patients. The use of machine learning in medicine is
increasing because they can identify patterns in data.
Using machine learning to predict heart disease could
help doctors reduce risk. This study aims to analyze
various aspects of patient data to provide accurate
predictions of heart disease. Based on our analysis, the
most important predictors of cardiovascular disease
were identified using the most selective correlation
methods and best-in-class studies. Studies have found
that the most important factors in the diagnosis of heart
disease are age, gender, smoking, obesity, diet, physical
activity, stress, type of chest pain, previous chest pain,
diastolic blood pressure, diabetes, troponin,
electrocardiogram and targets. This program can be
used as an early prediction of heart disease.
Keywords :
Cardiovascular, Artificial Intelligence, Logistic Regression, Naive Bayes, K Nearest Neighbor, Multilayer Perceptron.
