Intuitionistic Fuzzy Control of Different Strategies for Cancer Treatment | ||
| Menoufia Journal of Electronic Engineering Research | ||
| Article 1, Volume 30, Issue 2, July 2021, Pages 1-9 PDF (1.11 M) | ||
| Document Type: Original Article | ||
| Authors | ||
| Ahmed Hamdy El-Garawany* 1; Mohamed A El-Brawany2 | ||
| 1industrial electronics and control department , faculty of electronic engineering, menoufia university | ||
| 2Department of Industrial Electronics, Faculty of electronic engineering, Menoufia. University | ||
| Abstract | ||
| Chemotherapy is one of the most effective treatment methods for cancer patients. An intuitionistic fuzzy logic control (IFLC) of chemotherapy drug delivery system is presented in this paper. An Ant Colony Optimization (ACO) algorithm is utilized to optimize the intuitionistic fuzzy input-output scaling factors of the drug infusion system. The controller is designed and tested for tracking two clinically relevant chemotherapy dose scheduling protocols. Moreover, the patient safety is implied in the design of our controller by considering maximum allowed level constraints on both drug infusion dose and drug-dose toxicity. The obtained results from the optimized control system are compared with the previous studies. The application of the developed controller resulted in lowering the number of remaining cancer cells to 0.1856. with the highest performance index of 29.31. | ||
Highlights | ||
An intuitionistic fuzzy controller has been utilized to improve the drug infusion control system using. cancer patient model. The objective of controller to kill maximum number of remain cell and minimizing the side-effects of the drug. This has been achieved by adding an optimizer (ACO) to tune the PID-IFLC controller parameters. In this way, the ACO optimization is built to boost the search area and avoid local optima points. Drug dose, toxicity and concentration of drugs were always below the human's allowable limits. The future study will include the use of the established controller in animal studies using a commercial chemotherapy drugs infusion system. | ||
| Keywords | ||
| Cancer Treatment; Chemotherapy; Drug Delivery System; Adaptive intuitionistic Fuzzy; Ant Colony Optimization | ||
| Full Text | ||
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Cancer is a fetal disease, where number of abnormal cells start to be out of control and unfold into encompassing vital tissues. Cancer is one of the world's leading causes of mortality rates and death, with approximately 1,762,450 new cancer cases and 606,880 cancer deaths are estimated to happen in the United States [1] in 2019 . There are many techniques current used to tackle cancer, like surgery, radiation and chemotherapy.
Chemotherapy is an effective treatment type for fast spreading cancer. On the other hand, both surgery and radiation therapy remove, kill, or damage cancer cells in a specific area [2]. Use of chemotherapy drugs interrupts the cancer cell growth. Nevertheless, chemotherapy has two remarkable drawbacks that are drug resistance and the toxicity of drug [3]. All chemotherapy not only kill the cancerous cells, but they also have a bad effect on normal cells and destroy them. In contrast, inappropriate chemotherapeutic drugs can cause mutation of tumor cells.
The injection of chemotherapeutic agents into vein of the patient is referred to the intravenous chemotherapy clinical treatment. While drug dose scheduling plays a significant role to achieve an equilibrium between sparing the normal cells and killing the cancer cells with minimum level of toxicity and drug resistance impact [4]. The traditional intravenous infusion systems consist of a fluid container, administrator set, and a clamp to control the flow rate from the set to the patient. The major difficulty with this infusion system is that the flow cannot be accurately controlled and are always prone for human errors. To overcome this inaccuracy problems, electronic drug delivery systems are often used to regulate infused drug dose during chemotherapy sessions [5].
Mathematical modelling and control methods can be used to identify appropriate drug dose schedules for cancer treatment. Swierniak et al [6] and Araujo et al [7] provide reviews of how mathematical models were used to develop chemotherapeutic treatment schedules.
Drug resistance and toxicity, as already mentioned, are two significant issues that limit chemotherapy treatments. However, some earlier studies only took one of them into account. Batmani and Khaloozadeh [8] found certain optimal treatment regimens for cancer patients. But in their study, they did not consider the issue of drug resistance. Khaloozadeh et al. [9] achieved the best drug regimens taking into account two target functions to minimize the number of tumor cells and to protect normal cells. However, the drug resistance was also ignored in their models.
Martin [10] introduced an optimal chemotherapy drug scheduling model and solved it by applying nonlinear programming techniques. But he found difficulty while using these nonlinear techniques and hard to get a simplistic optimal control solution for all cancer chemotherapy drug scheduling problems. Tan et al [11] proposed a distributed evolutionary software that obtains automated solutions to the complex problem for the scheduling of chemotherapies. Liang et al. [12] updated the response model for chemotherapy in [10], because the drug toxicity modelling did not conform to relevant clinical work. This research team has been successful in achieving better outcomes by optimizing the schedule of chemotherapeutic agents using developed evolutionary algorithm; namely adaptive elitist-population-based genetic algorithms (AEGA) under specific toxicity tolerance [13]. Batmani et al. [14] extended the model proposed by Westman et al [15] to forecast the dynamics of tumor growth in the presence of chemo treatment and then used Non-dominated genetic sorting to fix the
problem of bi-objective optimization. Other methods have
also been used for the treatment of cancer chemotherapy,
such as improved immune algorithms [16] and the Multi-
Objective Evolutionary Approach (MOEA) [17].
But these open-loop systems for medication delivery can't
cope with patient variability. Moreover, they can't handle
any unforeseen health circumstance, such as a rise in
drug toxicity in the human body. In contrast, closed loop
systems [18-20] contain a feedback mechanism, more
accurate, robust in the existence of non-linearity and
external noise sources. According to these features, closed
loop control systems present a good alternative to safely
achieve cancer chemotherapy treatment goals. Algoul et
al. [21] applied PID and IPD controllers with a multiobjective
genetic algorithm in the closed loop
chemotherapy system to adjust controller parameters. The
optimized PID controller was applied successfully to
Martin’s chemotherapy response model. However, one
suitable solution for PID parameters must be carefully
chosen from several optimal Pareto solution sets.
Khadraoui et al [22] applied two PID controllers on a
modified Martin’s chemotherapy model to control both
toxicity and drug concentration in patient body. But the
authors in [21, 22] used conventional controller that
usually does not obtain satisfactory performance under
parameter variations [23].
Fuzzy logic controllers can work with imprecise inputs,
handles non-linearities and is more robust than
conventional controllers[24]. Fuzzy PID controllers have
self-tuning ability and on-line adaptation to nonlinear,
time varying, and uncertain systems [25]. However, fuzzy
sets suffer from the restriction of the uncertain element
because the lack of information. Uncertainty is an
inseparable aspect of medical problems [26]. Among
extensions of fuzzy sets, Atanassov’s intuitionistic fuzzy
sets [27] provide an intuitive structure for dealing with
ambiguity from imprecise knowledge by taking nonmembership
into account as well as membership values.
In this study, a PID-like IFLC controller is utilized to
control chemotherapy drug delivery system in a closed
loop. An ACO optimization is used to tune the
controller’s parameters to achieve the best results from
drug infusion process. Also, the control system is
designed to achieve both clinical and cancer therapy
needs.
The following is the organization of this paper. Section 2
explains the cancer chemotherapy drug scheduling model
and the controller design in the closed loop infusion
system. Section 3 presents the results of the developed
control system and compares them with the results of
previous works. Discussion is given in Section 4. Section
5 presents conclusion and comments on future work.
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