Heart Arrhythmia Detection Through Real-Time ECG Acquisition by Machine Learning Techniques

Heart disease illness depicts a scope of conditions that influence your heart. Machine learning (ML) and deep learning end up being powerful in helping to simply decide and forecast from the enormous amount of information delivered by the medical services industry. In this paper, we compared the accuracies of seven algorithms viz. Logistic Regression, KNN (nearest neighbor), Naive Bayes Classifier (NBC), Decision tree, Random Forest, Support Vector Machine (SVM), and Convoluted Neural Networks, to pick the best suitable algorithm and finally we chose CNN (Deep Learning or CNN, i.e., Convolutional Neural Network) which has shown outstanding results with an accuracy of 99.96% in binary classification and 97.08% in multi-class classification. A robust and accurate system to detect heart diseases on real-time data is developed which is based on CNN.