A Knowledge-Based System for the Classification of Cancer Diseases in Human Body Using Hybrid Approaches Based on Bipolar Complex Fuzzy Information

Abstract

Cancer is a collection of diseases in which cells separate without mechanism or order. The over-division of cells affects information recognized as a tumor. Not every tumor is considered cancer. To be identified as cancer, a tumor must damage neighboring muscles or tissues. International collaboration on cancer reporting (ICCR) information organization has been utilized to give a dominant, indication-based technique for the converging of carcinoma. The influence guarantees that the information organizations given for distinct tumor sorts have a dominant rule and style and includes all the parameters required to guide administration and prediction for separate cancers. This manuscript aims to expose certain new and valuable ideas with the help of bipolar complex fuzzy (BCF) information and power aggregation operators and evaluated their influential and dominant results and properties. Additionally, finding the most deadly and awkward sort of cancer in the world is very complicated. Thus, the main theme of the diagnosed operators is to evaluate the problem related to finding the deadliest type of cancer in human beings. Finally, we compare the final result with the certain prevailing result to enhance the worth of the evaluated approaches.

Keywords

• Bipolar complex fuzzy information
• power averaging/geometric aggregation operators
• classification of cancer diseases