Predicting Heart Disease Using Supervised Machine Learning Techniques: A Comparative Analysis
Abstract
Early diagnosis of diseases can improve patient outcomes and increase the chances of successful treatment.
One of the biggest causes of death worldwide is cardiovascular disease. Deep learning models have recently been
shown to be quite accurate at doing this task, and machine learning techniques are increasingly being used to predict
cardiac illness. The supervised learning algorithms KNN, Random Forest, Logistic Regression, SVM, and deep
learning model artificial neural networks are all compared in this research, for the prediction of heart disease. We used
a publicly available dataset of Cleveland Heart Disease Dataset on heart disease to train and test the models as well as
compare their performance in terms of various accuracy metrics. Random Forest got highest accuracy with 92.17%
and Logistic Regression with 88.4%, KNN with 90.0% and SVM with 90.08%, while deep learning model
outperformed with 98.92% accuracy. Our results show that across all models, Random Forest has the highest accuracy,
while deep learning models beat other supervised learning techniques in terms of overall accuracy. Additionally, we
developed a web based model and integrated the model with web based for further analysis and research purposes. We
learn that the best model to use relies on the specifics of the task and the available data and that mixing different
models might lead to even better performance gains. Our study clarifies the advantages and disadvantages of different
machine learning methods for predicting heart disease, and it may aid in the development of more accurate and reliable
prediction systems for use in clinical settings.
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