Authors :
Anika Shreya Pawar
Volume/Issue :
Volume 7 - 2022, Issue 8 - August
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3dfcof7
DOI :
https://doi.org/10.5281/zenodo.7073448
Abstract :
Machine learning (ML) is an artificial
intelligence (AI) technique that facilitates the
improvement of predictability in software applications
without requiring explicit programming. Data from the
past is used to predict new outcomes using machine
learning algorithms. During the course of this paper, we
used four machine learning algorithms: Logistic
Regression, Support Vector Machine, Decision Tree,
and Gradient Boosting. Our chosen algorithms were
applied to five datasets from the healthcare domain, in
which we were able to predict kidney disease, liver
disease, breast cancer predictions, heart diseases, and
diabetes predictions.
Keywords :
Machine Learning; kidney; heart; liver; breast- cancer; diabetes; svm; linear regression; decision tree; gradient boosting.
Machine learning (ML) is an artificial
intelligence (AI) technique that facilitates the
improvement of predictability in software applications
without requiring explicit programming. Data from the
past is used to predict new outcomes using machine
learning algorithms. During the course of this paper, we
used four machine learning algorithms: Logistic
Regression, Support Vector Machine, Decision Tree,
and Gradient Boosting. Our chosen algorithms were
applied to five datasets from the healthcare domain, in
which we were able to predict kidney disease, liver
disease, breast cancer predictions, heart diseases, and
diabetes predictions.
Keywords :
Machine Learning; kidney; heart; liver; breast- cancer; diabetes; svm; linear regression; decision tree; gradient boosting.