Correlation Analysis for Determining Effective Data in Machine Learning: Detection of Heart Failure

Heart disease is one of the causes for death throughout the world. Heart disease cannot be easily identified by the medical experts and practitioners as the detection of heart disease requires expertise and experience. Hence, developing better performing models for heart disease detection using machine-learning algorithms is crucial for detecting heart disease in an early stage. However, employing machine learning algorithm involves determining the relationship between the heart failure dataset features. In this study, correlation analysis is employed to identify the relationship among the heart failure dataset features and a predictive model for heart failure detection is developed with K-nearest neighbor (KNN). Pearson correlation is employed to identify the relationship between the features in the heart failure dataset and the effect of strong correlation to the target feature on the performance of K-nearest neighbor (KNN) model is analyzed. The experimental result shows that highly correlated feature significantly affected the performance of K-nearest neighbor (KNN) for heart failure detection. Finally, the performance of KNN is evaluated and result reveals that the model has acceptable level of performance with highest accuracy of 97.07% on heart failure prediction.