Authors :
Varun Chavan; Niyati Doaj; Naveen Vaswani
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/ym7mzfvm
Scribd :
https://tinyurl.com/ywh2p43r
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR2107
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Nowadays, cardiovascular diseases are the
major concern for Human beings. Cardiovascular Heart
Diseases (CHDs) are the major reason for mortality
globally causing millions of deaths each year. The global
death toll from Cardiovascular Heart Disease (CHD)
rose from 12.1 million in 1990 to 20.5 million in 2021.
The age-standardized death rate for CHD in India was
282 deaths/100000 was higher compared with global
levels that is 233 deaths/100000. The leading factors
contributing to cardiovascular diseases or fatalities
encompass high cholesterol level, triglyceride, etc. These
are the most common factors nowadays which result in
heart attack in humans. The main aim of this particular
project is to explore various risk factors associated with
myocardial infarction, commonly known as Heart
Attack. We have used tensorflow and Keras frameworks
for the following project. In this project, we utilize
neural network models to forecast the risk of coronary
heart disease (CHD) using various features. The
prediction process involves training the models on a
portion of the data and assessing their effectiveness on
the remaining test set. These models are designed to
discern patterns and correlations between input features
and the target variable, 'chd'. The choice of features,
architecture, and training parameters influences the
model's predictive performance. The research focuses on
two distinct neural network models: the first,
'sbp_model,' predicts CHD using only systolic blood
pressure ('sbp'), while the second, 'linear_model,' utilizes
all available features after normalization. Both models
are evaluated on their ability to predict CHD through
mean absolute error, with training histories and loss
curves analysed. We have taken into consideration all
the important Regression Models.
Keywords :
Cardiovascular Diseases, CHD, Regression Model.
Nowadays, cardiovascular diseases are the
major concern for Human beings. Cardiovascular Heart
Diseases (CHDs) are the major reason for mortality
globally causing millions of deaths each year. The global
death toll from Cardiovascular Heart Disease (CHD)
rose from 12.1 million in 1990 to 20.5 million in 2021.
The age-standardized death rate for CHD in India was
282 deaths/100000 was higher compared with global
levels that is 233 deaths/100000. The leading factors
contributing to cardiovascular diseases or fatalities
encompass high cholesterol level, triglyceride, etc. These
are the most common factors nowadays which result in
heart attack in humans. The main aim of this particular
project is to explore various risk factors associated with
myocardial infarction, commonly known as Heart
Attack. We have used tensorflow and Keras frameworks
for the following project. In this project, we utilize
neural network models to forecast the risk of coronary
heart disease (CHD) using various features. The
prediction process involves training the models on a
portion of the data and assessing their effectiveness on
the remaining test set. These models are designed to
discern patterns and correlations between input features
and the target variable, 'chd'. The choice of features,
architecture, and training parameters influences the
model's predictive performance. The research focuses on
two distinct neural network models: the first,
'sbp_model,' predicts CHD using only systolic blood
pressure ('sbp'), while the second, 'linear_model,' utilizes
all available features after normalization. Both models
are evaluated on their ability to predict CHD through
mean absolute error, with training histories and loss
curves analysed. We have taken into consideration all
the important Regression Models.
Keywords :
Cardiovascular Diseases, CHD, Regression Model.
