Optimal adaptive intuitionistic fuzzy logic control of anti-cancer drug delivery systems. (April 2020)

• Record Type: Journal Article
• Authors: Karar, Mohamed Esmail; El-Garawany, Ahmed Hamdy; El-Brawany, Mohamed
• Is Part Of: Biomedical signal processing and control. Volume 58(2020)
• Publication Date: 2020-04
• Subjects: Cancer treatment -- Chemotherapy -- Drug delivery system -- Intuitionistic fuzzy sets -- Invasive weed optimization; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.101861
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A new controller has been developed for regulating closed-loop intravenous anti-cancer drug delivery. The developed drug delivery control system is based on intuitionistic fuzzy sets and invasive weed optimization algorithm. The developed controller is robust to handle possible physiological conditions and chemotherapy drug constraints. Performance of optimal adaptive intuitionistic fuzzy logic controller is superior to other methods in the previous studies. Abstract: This paper introduces a new closed loop fuzzy logic controller for regulating intravenous anti-cancer drug delivery, based on intuitionistic fuzzy sets and invasive weed optimization (IWO) algorithm. The developed intuitionistic fuzzy logic controller (IFLC) contributes the following advancements: First, the parameters of IFLC are adaptive and optimally tuned to achieve desired drug concentrations at the tumor site, killing almost all cancer cells at the end of treatment time. Second, drug delivery constraints; namely allowed levels of the drug dose and cumulative toxicity are naturally implied in the design of the optimized IFLC to ensure the clinical safety of cancer patients. Finally, the developed drug delivery control system is robust to handle all possible physiological conditions during treatment such as patient sensitivity in response to toxicity. To test and validate the performance of developed IFLC, extensive simulation results and comparative evaluation have been done on a mathematical patient model. It is proved that our optimal adaptive IFLC is superior to other methods in previous related studies, resulting in the best performance index and the minimum number of remaining cancer cells of 27.63 and 0.806, respectively.