Cardiovascular Disease Prediction using Various Machine Learning Algorithms

Swain, Debabrata, Parmar, Badal, Shah, Hansal and Gandhi, Aditya (2022) Cardiovascular Disease Prediction using Various Machine Learning Algorithms. Journal of Computer Science, 18 (10). pp. 993-1004. ISSN 1549-3636

[thumbnail of Cardiovascular Disease Prediction using Various Machine Learning Algorithms..pdf] Text
Cardiovascular Disease Prediction using Various Machine Learning Algorithms..pdf

Download (1MB)


Abstract: Almost one-third of all deaths caused around the world were
caused due to cardiovascular diseases. Even if death was not the result, much
cost is incurred during the treatment of such diseases. But much of these
deaths and treatments could have been prevented with prior action. Advance
knowledge of the symptoms and consequently proper care can lead us to
avoid such diseases. Thus, current research proposes a highly effective model
to predict the presence of heart diseases. Bad eating habits, smoking, stress,
and genetics are some of the factors that influence our body mechanisms,
which actually cause various irregularities in our hearts and thus adversely
affect our bodies. The body mechanisms influenced by external factors have
been included to prepare an efficient model to predict the probability of
cardiovascular diseases. UCI repository dataset has been utilized for the
training and testing purpose in our model. Then accordingly, five different
algorithms namely Logistic Regression, Support Vector Machine, MultiLayer Perceptron (MLP) Classifier with Principal Component Analysis
(PCA), Deep Neural Network, Bootstrap Aggregation using Random Forests
are executed on our filtered dataset to find which one is the optimum out of
all of them. Pre-processing techniques have been extensively used to filter
out the dataset. The data processing along with the different models
employed make this a sound paper, which could be utilized for real-world
cases without any prior modification. Different places around the world
would take different factors into account, hence our model can be used as it
takes all critical factors from several datasets.

Affiliation: Skyline University College
SUC Author(s): Pradhan, M R ORCID:
All Author(s): Swain, Debabrata, Parmar, Badal, Shah, Hansal and Gandhi, Aditya
Item Type: Article
Uncontrolled Keywords: Cardiovascular Disease Prediction, Aggregated Dataset, Machine Learning Algorithms, Deep Learning, Bootstrap Aggregation using Random Forests, Logistic Regression, Deep Neural Network, MLP with PCA, SVC
Subjects: B Information Technology > BA Information Systems
B Information Technology > BB Information Technology
B Information Technology > BL Machine Learning
B Information Technology > BM Artificial Intelligence
B Information Technology > BR Deep Learning
Divisions: Skyline University College > School of IT
Depositing User: Mr Mosys Team
Date Deposited: 11 Dec 2023 06:05
Last Modified: 11 Dec 2023 06:05
Publisher URL:
Publisher OA policy:
Related URLs:

    Actions (login required)

    View Item
    View Item
    Statistics for SkyRep ePrint 584 Statistics for this ePrint Item