Enhanced Knowledge Based System for Cardiovascular Disease Prediction using Advanced Fuzzy TOPSIS - Parveen, Huma; Rizvi, Syed Wajahat Abbas; Boddu, Raja Sarath Kumar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Cases of cardiovascular diseases have risen over the last decade, making them the leading cause of mortality. Early detection of heart diseases helps doctors provide better treatment to patients. In this research, a knowledge-based hybrid heart disease prediction model using advanced fuzzy techniques and artificial neural networks (ANN) is proposed. The ANN and advanced fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques are implemented in the proposed methodology for risk prediction of disease and disease classification, respectively. The Analytic Hierarchy Process (AHP) method's attribute weights help make effective prediction of diseases. The proposed model (ANN+ fuzzy TOPSIS) has been measured over various performance-measuring metrics and then compared to other traditional techniques to determine its efficiency. Numerical analysis of the proposed model shows that it performed better than other conventional methods in terms of accuracy (0.99), precision (0.98), specificity (0.978), F-measure (0.981), sensitivity (0.996), and many more. The goal of this research is to improve knowledge-based systems' efficacy through the application of fuzzy logic and ANN for cardiovascular disease prediction and classification.