A Survey on Early Detection and Prediction of Heart Diseases using Machine Learning and Data Mining Techniques
DOI:
https://doi.org/10.26438/ijcse/v8i2.3134Keywords:
Heart Disease, Predictive Analysis, Naïve Bayes, Decision Tree, SVMAbstract
Cardio vascular disease is the most prominent cause of death worldwide. Machine Learning Algorithms can be used for predicting chances of heart disease occurrence. Relating machine learning and data mining methods is a strategic approach to consume large volumes of available Cardio-related data for prediction. The datasets used are classified in terms of medical parameters. In this paper, numerous algorithms and techniques are discussed that are used in prediction of Cardio Vascular Diseases. Fast Correlation-Based Feature Selection (FCBF) method to filter noise data to improve quality of heart disease classification. K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Random Forest and a Multilayer Perception, Artificial Neural Network optimized by Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO) are the classification algorithms used. By using machine learning algorithms and deep learning it provides numerous ways for the prediction of the heart disease. There are various methods which provide us an information and these are applied to various datasets to get particular results.
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