Authors :
Kiran Londhe; Aakash Kadam
Volume/Issue :
Volume 8 - 2023, Issue 3 - March
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3KumWnw
DOI :
https://doi.org/10.5281/zenodo.7811138
Abstract :
Predicting and detection of heart disease has
always been a critical and challenging task for
healthcare practitioners. Hospitals and other clinics are
offering expensive therapies and operations to treat
heart diseases. S o, predicting heart disease at the early
stages will be useful to the people around the world so
that they will take necessary actions before getting
severe. Heart disease is a significant problem in recent
times; the main reason for this disease is the intake of
alcohol, tobacco, and lack of physical exercise. Over the
years, machine learning shows effective results in
making decisions and predictions from the broad set of
data produced by the health care industry. S ome of the
supervised machine learning techniques used in this
prediction of heart disease are artificial neural network
(ANN), decision tree (DT), random forest (RF), support
vector machine (SVM), naïve Bayes) (NB) and k- nearest
neighbour algorithm. Furthermore, the performances of
these algorithms are summarized.
Keywords :
Machine Learning, Supervised Learning, Health Care Services, Heart Disease.
Predicting and detection of heart disease has
always been a critical and challenging task for
healthcare practitioners. Hospitals and other clinics are
offering expensive therapies and operations to treat
heart diseases. S o, predicting heart disease at the early
stages will be useful to the people around the world so
that they will take necessary actions before getting
severe. Heart disease is a significant problem in recent
times; the main reason for this disease is the intake of
alcohol, tobacco, and lack of physical exercise. Over the
years, machine learning shows effective results in
making decisions and predictions from the broad set of
data produced by the health care industry. S ome of the
supervised machine learning techniques used in this
prediction of heart disease are artificial neural network
(ANN), decision tree (DT), random forest (RF), support
vector machine (SVM), naïve Bayes) (NB) and k- nearest
neighbour algorithm. Furthermore, the performances of
these algorithms are summarized.
Keywords :
Machine Learning, Supervised Learning, Health Care Services, Heart Disease.
