Abstract:
Physical diseases including heart disease have been on the rise recently. The subject is
well-known in the modern world. The majority of individuals have an issue with heart
disease. The discrepancies between the normal and afflicted diagnosis report ratios serve
as a gauge of the condition. Heart illness is a condition that has been the subject of
several investigations in the past. We have identified a few excellent chances to develop
the methodology. We suggest employing efficient algorithm models to forecast dangers
and raise early awareness. Our suggested approach is suited for straightforward heart
disease predictions and is simple to apply in the actual world. The Kaggle website hosted
the dataset. In our model, we have implemented some different classifiers named
Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), Support Vector
Classifier (SVC), Adaboost Classifier (ABC), Naïve Bayes (NB), Decision Tree (DT)
algorithms. Random Forest (RF) given an accuracy of 90.22%, Logistic Regression (LR)
given accuracy of 89.67%, Gradient Boosting (GB) given accuracy of 89.67%, Support
Vector Classifier (SVC) given accuracy of 91.85%, Adaboost Classifier (ABC) given the
accuracy 91.30%, Naïve Bayes (NB) given the accuracy 89.67%, Decision Tree (DT)
given the accuracy 91.85%. We have used ensemble techniques to get the best accuracy.
Our voting classifier RDSGLGA gave the best accuracy of 93.478%. Another voting
classifier RDS gave an accuracy of 92.39%. To assign the optimal parameters to each
classifier, we employed hyperparameter tuning. The experimental investigation reviewed
the results of previous recent studies and found that RDSGLGA performed best, with an
accuracy rate of 93.478% in terms of making heart disease predictions.
