**1. Introduction**

Cancer has a long and complicated history: it appeared and was recognized even in the ancient times. Modern science has carried out some significant amounts of research on tumors and their treatment. The classic triplet of the disease's treatments is surgery, radiation, and chemotherapy, which are constantly supplemented by more and more advanced methods. Modern oncology has, at its disposal, a wide arsenal of tools and methods for treating cancer: for saving human life, they help to prevent its occurrence and development; in hopeless cases, they prevent the maximum extension and ease the painful symptoms.

Due to the wide spread of oncological diseases, it is especially important to be able to detect cancer at the early stage, when it is more possible to completely heal the patient. Nowadays, cancer is the second leading cause of death in the world, after cardiovascular diseases. Cancer causes almost one in six deaths worldwide. According to the World Health Organization, the incidence of cancer in the next 20 years will increase by 70%. The State Statistical Committee also reports that in Azerbaijan, for every 100,000 people, there are over 400 patients with a malignant tumor; most of them are women. The conducted statistical data demonstrate that in our country, there is an increase in the number of patients diagnosed with cancer. Many experts believe that in a few years, malignant neoplasms will become the main cause of death worldwide, leaving cardiovascular diseases far behind. The worst thing is that the incidence of cancer is growing, but the survival rate is not increasing. In most cases, this occurs because of the late detection of the disease, as success in recovery strongly depends on the early diagnosis of asymptomatic cancer. The problem with the growing number of cancer patients should be solved not only by medicine but also by all sciences that can help in the fight against this cruel disease. This work is specifically aimed at helping oncologists in making an accurate diagnosis at early stages and possibly saving someone's life.

Nowadays, one of the most spread cancer-related infections is colorectal cancer (CRC). The statistics of this illness is studied in [1], and it has been found that CRC should be more investigated among the young generation.

In the other research [2], risk factors that affect development of CRC are analyzed. In the research, the risk for growth of cancer is defined, but patients' gender wasn't taken into consideration. Thus, a more accurate analysis of colorectal cancer is required.

Information about the illness is discussed in [3–9]. The authors used two data-driven approaches: logistic regression and neural network. The effectiveness of logistic regression in the study appeared to be near 66%; the effectiveness of neural approach was 78%. The study was performed on the data obtained from 403 patients. The results demonstrate superior effectiveness of neural networks in comparison with logistic regression when applied to cancer diagnostics. In general, neural networks have several advantages: ability to process vast amounts of information, fault tolerance, generalization ability, adaptability, and learning. In the discussed studies and applied methods, crisp statistic information was used; but data on patients are always rather inaccurate, which enables the applicability of fuzzy data.

There are several research studies on medical expert systems reported in scientific literature [10–14]. These research studies are based on linguistic information, fuzzy inference reasoning, and probability-based reasoning. However, these systems' performance is accompanied by the collateral information loss; thus, these studies possess some effectiveness limits. From this viewpoint, a possibility-measure-based fuzzy inference method seems to be more effective [15–19]. This measure-based algorithm is a kernel of information processing of the software system ESPLAN [20]. Possibility measure is a fuzzy measure and can partially operate Z number-based information. Zadeh's last theory [21] is an extension of fuzzy logic and able to represent different types of information uncertainties. Processing of information based on possibility measure might be quite effective in medicine.

The purpose of this study is to design a fuzzy rule-based expert system for diagnosis of colorectal cancer based on possibility measure and data extracted from Big Data. The rest of the paper is organized as follows. Section 2 briefly describes fuzzy c-means algorithm and the possibility-measure-based inference algorithm. Statement of the problem and its solution are given in Section 3. Finally, Section 4 concludes the paper.
