Authors :
S.Vaahnitha; T. Charitha Sri; G.V.Sai Kiran
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/2p8j8yva
DOI :
https://doi.org/10.5281/zenodo.8090758
Abstract :
Heart disease is one of the main causes of
death worldwide, and early diagnosis is essential for
successful treatment and the avoidance of unfavourable
effects. With the use of massive datasets, sophisticated
algorithms, and pattern recognition, machine learning
has become an effective tool for identifying and
diagnosing cardiac disease. Feature selection,
dimensionality reduction, and ensemble learning are
three machine learning approachesthat we integrate in
this studyto provide a unique method for detecting heart
disease. Our model outperforms current state-of-the-art
techniques in terms of sensitivity and specificity, as well
as high accuracy and resilience. Ourmethod is also very
interpretable and offers information on the under lying
causes of heart disease risk. These findings underscore
the significance of current research in this crucial area
and show how machine learning has the potential to
increase the precision and effectiveness of heart disease
identification.
Heart disease is one of the main causes of
death worldwide, and early diagnosis is essential for
successful treatment and the avoidance of unfavourable
effects. With the use of massive datasets, sophisticated
algorithms, and pattern recognition, machine learning
has become an effective tool for identifying and
diagnosing cardiac disease. Feature selection,
dimensionality reduction, and ensemble learning are
three machine learning approachesthat we integrate in
this studyto provide a unique method for detecting heart
disease. Our model outperforms current state-of-the-art
techniques in terms of sensitivity and specificity, as well
as high accuracy and resilience. Ourmethod is also very
interpretable and offers information on the under lying
causes of heart disease risk. These findings underscore
the significance of current research in this crucial area
and show how machine learning has the potential to
increase the precision and effectiveness of heart disease
identification.