Authors :
Narannagari Chaathurya; Sikharam Abhinav; Battu Sri Vamshidhar; Kandula Revathi
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/4mwtdba6
Scribd :
https://tinyurl.com/5n6s3m74
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR211
Abstract :
Over the past few decades, cardiovascular
disease has emerged as the primary cause of death
worldwide in both industrialized and developing nations.
Early detection of heart problems and continued clinical
monitoring can reduce death rates. However, because it
takes more time and experience, it is not possible to
accurately detect heart disorders in all cases and to have
a specialist talk with a patient for 24 hours. We
demonstrate how machine learning can be used to
estimate an individual's risk of developing heart disease.
This study presents data processing, which includes
converting categorical columns and working with
categorical variables. We outline the three primary
stages of developing an application: gathering datasets,
running logistic regression, and assessing the properties
of the dataset. The random forest classifier technique is
developed to diagnose cardiac problems more precisely.
Data analysis is needed for this application since it is
considered noteworthy. The random forest classifier
algorithm, which improves the accuracy of research
diagnosis, is next covered, along with the experiments
and findings.
Keywords :
Artificial Intelligence; Early Detection; Machine Learning; Heart Disease Detection; Data Analysis.
Over the past few decades, cardiovascular
disease has emerged as the primary cause of death
worldwide in both industrialized and developing nations.
Early detection of heart problems and continued clinical
monitoring can reduce death rates. However, because it
takes more time and experience, it is not possible to
accurately detect heart disorders in all cases and to have
a specialist talk with a patient for 24 hours. We
demonstrate how machine learning can be used to
estimate an individual's risk of developing heart disease.
This study presents data processing, which includes
converting categorical columns and working with
categorical variables. We outline the three primary
stages of developing an application: gathering datasets,
running logistic regression, and assessing the properties
of the dataset. The random forest classifier technique is
developed to diagnose cardiac problems more precisely.
Data analysis is needed for this application since it is
considered noteworthy. The random forest classifier
algorithm, which improves the accuracy of research
diagnosis, is next covered, along with the experiments
and findings.
Keywords :
Artificial Intelligence; Early Detection; Machine Learning; Heart Disease Detection; Data Analysis.
