A Review on Machine Learning Techniques for Prediction of Cardiovascular Diseases

Cardiovascular disease is a major cause of death worldwide. The detection of these diseases at a premature phase is imperative to rescue the lives of people. Implying machine learning classification techniques into health care organization gives extraordinary results which assist health care professionals for immediate and accurate diagnosis of these diseases. Healthcare organizations generate a huge amount of data which is still not perfectly utilized by researchers. Machine learning techniques and tools help in extracting effective knowledge from datasets for more precise results. Exploring numerous combinations of algorithms and finding out efficient techniques from the recent research papers is the objective of this research. The novelty of our work is associated with uses of optimization algorithms over classification algorithms such as Genetic algorithm (GA), Naïve Bayes (NB), Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machine SVM), etc. used so far. Feature optimization techniques (Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) with machine learning techniques (K-Nearest Neighbor (KNN) and Random Forest (RF)) give maximum accuracy of 99.65% which is examined from the survey work. The future works can emphasize on developing an advanced model by integrating different optimization techniques using machine learning which could help the health care professionals in making felicitous decisions.