Improving Heart Attack Prediction Accuracy Performance Using Machine Learning and Deep Learning Algorithms

Accurate classification of cardiovascular diseases (CVDs) is of utmost importance for cardiologists to provide appropriate treatments. Diagnosing and predicting cardiovascular conditions are crucial medical responsibilities in this context. The healthcare sector is increasingly utilizing deep learning (DL) and machine learning (ML) algorithms due to their ability to identify patterns in data. Diagnosticians may reduce the number of misdiagnoses by using DL and ML techniques for the categorization of cardiovascular disease incidence. To reduce the mortality linked to CVDs, this research offers a unique model that properly predicts and classifies these problems. This research presents approaches such as deep learning, random forest (RF), support vector machines (SVM), and K-nearest neighbors for predicting heart disease arrest. We implemented the suggested model using 303 real-world instances from Kaggle. In the testing stage, the KNN model's accuracy was 92%. By comparison, the accuracy of the DL model was 87%. The RF model's accuracy was 84%. The results indicate that the KNN algorithm outperforms other algorithms in terms of accuracy. We compared the study's results with a variety of existing systems.