Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates

The trade-off between computation time and predictive accuracy is important in the design and implementation of clinical decision support systems. Machine learning techniques with incremental updates have proven its usefulness in analyzing large collection of medical datasets for diseases diagnosis. This research aims to develop a predictive method for heart disease diagnosis using machine learning techniques. To this end, the proposed method is developed by unsupervised and supervised learning techniques. In particular, this research relies on Principal Component Analysis (PCA), Self-Organizing Map, Fuzzy Support Vector Machine (Fuzzy SVM), and two imputation techniques for missing value imputation. Furthermore, we apply the incremental PCA and FSVM for incremental learning of the data to reduce the computation time of disease prediction. Our data analysis on two real-world datasets, Cleveland and Statlog, showed that the use of incremental Fuzzy SVM can significantly improve the accuracy of heart disease classification. The experimental results further revealed that the method is effective in reducing the computation time of disease diagnosis in relation to the non-incremental learning technique.