An Integrated Review On Machine Learning Approaches For Heart Disease Prediction: Direction Towards Future Research Gaps - A, Fathima (Computer Science and Information Technologies)
There has recently been a rapid increase in the count of statistical models obtainable for the prediction of heart disease. However, without a comprehensive overview, it remains unclear which, if any, should be applied in clinical care. Hence, this paper plans to make a clear literature review on state-of-the-art heart disease prediction models. It makes a plan to review 61 research papers and states a significant analysis. Initially, the analysis addresses the contributions of each literature works with its limitations and observes the simulation environment in which each contribution executes. Here, different types of machine learning algorithms deployed in each contribution are analyzed and state those limitations. In addition, the dataset utilized for existing heart disease prediction models are observed. Later the performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure etc are learned, and further, the best performance is also checked to confirm the effectiveness of entire contributions. Finally, comprehensive research challenges and the gap is portrayed based on the development of intelligent methods concerning the unresolved challenges in the case of heart disease prediction using data mining techniques.