Algorithm for early diagnosis of hepatocellular carcinoma based on gene pair similarity

The article proposes an algorithm based on intelligent methods for the early diagnosis of hepatocellular carcinoma (HCC), known as liver cancer, which is rated third cause of cancer deaths in the world. Initial diagnosis of HСC is based on laboratory studies, computer tomography and X-ray examination. However, in some cases, identifying cancerous tissues as similar non-cancerous tissues (cirrhotic tissues and normal tissues) made it necessary to perform gene analysis for the diagnosis. To predict HCC based on such numerous, diverse and heterogeneous unstructured data, preference is given to the method of artificial intelligence, i.e., machine learning. It shows the possibility of applying machine learning methods to solve the problem of accurate identification of HCC due to the compatibility of HCC tissues with identical CwoHCC non-cancerous tissues. The technology of gene pair profiling using relevant peer databases is described and the Within-Sample Relative Expression Orderings (REO) technique is used to determine the gene pair’s similarity. The article also presents a new approach based on The Within-Sample Relative Expression Orderings technique for determining the gene pair’s similarity, Incremental feature selection method for feature selection, and Support Vector Machine methods for gene pair classification. The proposed approach constitutes the methodological basis of a decision support system for the early diagnosis of HCC, and the development of such a system may be beneficial for physician decision support in the relevant fiel

Development of the principles of fuzzy rule-based system for hepatocelular carcinoma staging

The article proposes the principles for the development of a fuzzy rule-based physician decision support system n to determine the stages of the most common hepatocellular carcinoma (HCC) among malignant tumors of liver. The stages of HCC, i.e., critical situations, are expressed by different combinations of clinical signs of input data and emerging clinical conditions. These combinations shape the multiplicity of possible situations (critical situations) by forming linguistic rules that are in fuzzy relations with one another. The article presents the task of developing a fuzzy rules-based system for HCC staging by classifying the set of possible situations into given classes. In order to solve the problem, fuzzy rules of clinical situations and critical situations deviated from them are developed according to the possible clinical signs of input data. The rules in accordance with the decision-making process are developed in two phases. In the first phase, three input data are developed: nine rules are developed to determine possible clinical conditions based on the number, size, and vascular invasion of tumor. In the second phase, seven rules are developed based on possible combinations of input data on the presence of lymph nodes and metastases in these nine clinical conditions. At this stage, the rules representing the fuzzification of results obtained are also described. The latter provide an interpretation of results and a decision on related stage of HCC. It also proposes a functional scheme of fuzzy rules-based system for HCC staging, and presents the working principle of structural blocks. The fuzzy rule-based system for HCC staging can be used to support physicians to make diagnostic and treatment decision