Adaptive UKF Based State Estimation of HIV, Hepatitis-B and Cancer Mathematical Models

Abstract

Nowadays, mathematical model based estimation and control approaches are frequently consulted and applied for the treatment of such diseases. For the derived dynamics of the diseases, there are some states or internal variables which are very difficult to measure and needs very expensive measurement devices. Therefore, in this paper, adaptive unscented Kalman filter (AUKF) is designed for the state estimation of some vital diseases. These are Human Immunodeficiency Virus (HIV), Hepatitis-B virus (HBV) infection and Cancer such that unmeasurable states are estimated under measurement noises. The computational results show that accurate estimation of the unmeasured states are obtained and plotted for monitoring and control of possible future real-time applications.