Evaluation of Intensified Colorectal Cancer Treatment Using Model Based on Delphi Method, Fuzzy Logic, and Analytical Hierarchy Process (DFAHP)

Abstract

Colorectal cancer is one of the major fatal diseases in Taiwan, and the age of patients is decreasing yearly. As the early symptoms of malignant intestinal swelling are not obvious, most patients are not diagnosed with colorectal cancer until they are in a serious condition, and early diagnosis of colorectal cancer is a major challenge. In addition to genetic factors, colorectal cancer is caused by stress and poor dietary habits. To improve the effectiveness of treatment, we propose a model for multi-attribute decision-making based on medical data by combining the Delphi method, fuzzy logic theory, and the analytical hierarchy process. In the hierarchical structure, key risk factors and their values are proposed using fuzzy logic theory. The model suggests the importance of the medical team, medical equipment, post-operative care, and other factors for treating colorectal cancer effectively. Treatment and appropriate care must be integrated in the overall medical process to improve the effectiveness of colorectal cancer treatment.

Entity Relationship Modeling for IoT Data Fusion Driven by Dynamic Detecting Probe

“…Delphi method relies on domain-specific knowledge, and when the data source is not regular, it cannot be well adapted to the data, while weighted average, due to its fixed form, is more homogeneous for data processing and cannot make full use of the characteristics of the data. In contrast, fuzzy logic can contain expert domain knowledge [21] and its ability to use multiple functions for data tting when processing data. Its adaptability is relatively good, so fuzzy logic is chosen to normalize the similar values of multiple dimensions.…”

Analytical Hierarchy Process Problem Solution

“…It is a well-known and widely used multi-criteria decision-making method in various fields, such as machine selection, supplier selection, ambulance allocation, and prioritization of nurses and other resources in health services [6]. For example, for the global forecast of diabetes patients for 2011-2030 [13], to evaluate the performance of hospitals in Taiwan [14], to determine the most appropriate treatment for rectal cancer treatment [15], to compare pregnant women's recommended delivery [16], to determine efficacy in colorectal cancer treatment [17], to determine dental treatment priorities [18], to determine the criteria to be used in the decision to admit COVID-19 patients to intensive care units [19], and to design and build a decision support system application [20].…”