A Pragmatic Approach of Data Imputation Using Fuzzy-Based Swarm for Heart Disease Classification

Abstract

In this study, we investigate the classification problem of heart disease with incomplete datasets. Our pragmatic approach is to exploit the potential of complete data for selecting relevant features in incomplete datasets. We define our approach by implementing fuzzy-based particle swarm optimization to impute missing values with tuning the exist structure with the data which leads to better solution. The FCM clustering is applied to identify the similar records in the complete dataset. We compared the Root Mean Square Error (RMSE) results of three different datasets with seven different ratios with range 1% to 20% of missing data. The experimental results provide the evidence that our approach performs better accuracy compared to other approach. The proposed method makes it possible to select relevant feature by offering good combination of its setting to classification of heart disease problem.