Authors :
M.Sangeetha; S.Arun Kumar; K. Pazhani Bharathi; P .Kumara Guru; P.Bhuvan Prakash Reddy
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/5n6kzntn
Scribd :
https://tinyurl.com/mr8s64rj
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR2016
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Machine Learning and artificial intelligence
have found valuable on variety of disciplines during their
growth, particularly in the light of massive increase in
data in recent years. It has the potential to be more
dependable in terms of producing quicker and more
accurate illness prediction judgments. Therefore, the use
of machine learning algorithms to forecast different
diseases is growing. Building a model can also aid in the
visualization and analysis of diseases to increase the
accuracy and consistency of reporting. This article has
looked into using several machine learning algorithms to
identify cardiac disease. This article's study has
demonstrated a step procedure. In a dataset on heart
disease initially prepared in the format needed to run
machine learning algorithms. The UCI is the source of
patient medical records and other data. The presence are
absence of heart disease in patients is then ascertained
using the heart disease dataset. Second, this paper
presents a number of noteworthy findings. The confusion
matrix is used to validate the accuracy rate of machine
learning methods, including Gradient Boosting Classifier,
Support Vector Machine, and Logistic Regression.
According to recent research, the Logistic Regression
method outperforms other algorithms in terms of
accuracy, yielding a high 95% rate. It also outperforms
the other four algorithms in terms of recall, precision,
and f1-score correctness. The difficult and future
research component of this project will be raising the
accuracy rates of the machine learning algorithms to
between 97% and 100%.
Keywords :
Machine Learning, Artificial Intelligence, Heart Disease , logistic Regression, KNN, Support Vector Machine.
Machine Learning and artificial intelligence
have found valuable on variety of disciplines during their
growth, particularly in the light of massive increase in
data in recent years. It has the potential to be more
dependable in terms of producing quicker and more
accurate illness prediction judgments. Therefore, the use
of machine learning algorithms to forecast different
diseases is growing. Building a model can also aid in the
visualization and analysis of diseases to increase the
accuracy and consistency of reporting. This article has
looked into using several machine learning algorithms to
identify cardiac disease. This article's study has
demonstrated a step procedure. In a dataset on heart
disease initially prepared in the format needed to run
machine learning algorithms. The UCI is the source of
patient medical records and other data. The presence are
absence of heart disease in patients is then ascertained
using the heart disease dataset. Second, this paper
presents a number of noteworthy findings. The confusion
matrix is used to validate the accuracy rate of machine
learning methods, including Gradient Boosting Classifier,
Support Vector Machine, and Logistic Regression.
According to recent research, the Logistic Regression
method outperforms other algorithms in terms of
accuracy, yielding a high 95% rate. It also outperforms
the other four algorithms in terms of recall, precision,
and f1-score correctness. The difficult and future
research component of this project will be raising the
accuracy rates of the machine learning algorithms to
between 97% and 100%.
Keywords :
Machine Learning, Artificial Intelligence, Heart Disease , logistic Regression, KNN, Support Vector Machine.
