Chapter 3 - Sensor-enabled biomedical decision support system using deep learning and fuzzy logic

Abstract

The integration of fuzzy logic and deep learning in biomedical decision support systems (BDSS) is a promising approach. Fuzzy logic allows for the inclusion of imprecise or uncertain information, while deep learning effectively learns patterns in large datasets. This chapter reviews the current state of integrating fuzzy logic and deep learning in BDSSs, with a focus on predicting cardiovascular diseases. The proposed approach combines fuzzy logic–based feature selection with a deep learning model for classification, enabling the handling of imprecise data and improving prediction accuracy. The chapter concludes that this integration can enhance the reliability and accuracy of BDSSs, leading to better outcomes. Additionally, the chapter discusses a sensor-enabled BDSS that incorporates data from wearable devices, medical imaging equipment, and biological sensors, demonstrating improved medical diagnosis and treatment planning through the integration of deep learning and fuzzy logic.