Authors :
Akshat Kishore
Volume/Issue :
Volume 8 - 2023, Issue 2 - February
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3kei5xy
DOI :
https://doi.org/10.5281/zenodo.7659288
Abstract :
In this paper, I discuss a method for detecting
cardiac illness utilising artificial intelligence and machine
learning algorithms, and making these systems publicly
available. We demonstrate how artificial intelligence may
be used to forecast if someone would get cardiac disease. A
python-based application is created for healthcare
research in this study since it is more dependable and aids
in tracking and establishing various kinds of health
monitoring apps. We demonstrate categorical variable
manipulation and the conversion of categorical columns in
data processing. We tested a range of machine learning
models to achieve the goal of the research and compared
the accuracy of each of these models to find the most
accurate. We outline the key stages of application
development, including the gathering of databases,
applying logistic regression, parameter tuning, assessing
the features of the dataset, deploying the model and
connecting it to the front-end using APIs.
Keywords :
Artificial Intelligence, Machine Learning, Healthcare, AI in Healthcare
In this paper, I discuss a method for detecting
cardiac illness utilising artificial intelligence and machine
learning algorithms, and making these systems publicly
available. We demonstrate how artificial intelligence may
be used to forecast if someone would get cardiac disease. A
python-based application is created for healthcare
research in this study since it is more dependable and aids
in tracking and establishing various kinds of health
monitoring apps. We demonstrate categorical variable
manipulation and the conversion of categorical columns in
data processing. We tested a range of machine learning
models to achieve the goal of the research and compared
the accuracy of each of these models to find the most
accurate. We outline the key stages of application
development, including the gathering of databases,
applying logistic regression, parameter tuning, assessing
the features of the dataset, deploying the model and
connecting it to the front-end using APIs.
Keywords :
Artificial Intelligence, Machine Learning, Healthcare, AI in Healthcare