Detection of Cardiovascular Diseases Using Machine Learning Approach

Various advanced computing techniques and capabilities have the deep impact in the field of medical sciences, especially in identifying human heart diseases. So that identifying heart related diseases accurately and in time may save the patients life’s and increases the chances of survival. However, manual approaches for identifying heart disease suffer from biases and variations between examiners. We can use various machine learning algorithms to overcome these issues in manual approaches These ML algorithms provide more accurate and efficient tools for identifying and analysing the patients with heart disease. To explore the potential of machine learning algorithms, the recommended study employed various techniques to identify and predict human heart disease using a comprehensive heart disease dataset. Sensitivity, specificity, F-measure and accuracy in classification can be used to examine the performance. We have used eight machine learning classifiers such as Ada boost, Extreme Gradient Boosting including Decision Tree, Logistic Regression, Linear Discriminate Analysis, Random Forest, Naïve Bayes, Support Vector Machine. The results demonstrated notable improvements in the prediction classifiers’ accuracy. This underscores the efficiency of machine learning algorithms in finding and predicting human heart disease. This research achieved improved accuracy heart disease prediction using the machine learning technique. Multiple classifiers were employed to classify heart disease prediction, with SVM achieving an accuracy of 95.88%.