A Machine Learning-Based Technique for Predicting Heart Disease

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.