Fuzzy Decision Tree Based Method in Decision-Making of COVID-19 Patients’ Treatment

An interpreter ranking index-based MCDM technique for COVID-19 treatments under a bipolar fuzzy environment

The entire world is currently fighting the severe and dangerous pandemic COVID-19, which is causing bodily suffering and mental distress due to the rapidly increasing number of infected patients and deaths worldwide. Many COVID-19 treatments are going on in India, and some treatments are under development for these patients. But, treatment selection for the COVID-19 patients is challenging in the present situation. Through the multi-criteria decision-making technique, they can select the COVID-19 treatments easily. Therefore, we have developed an MCDM technique to select COVID-19 treatments in India. This paper invented the value and ambiguity of bipolar fuzzy (BF) numbers. Additionally, some fundamental theorems and properties of BF-numbers are studied. A novel positive and negative interpreter ranking index of BF numbers has been introduced. In the present day, most human decision-making relies heavily on bipolar fuzzy information. Hence, we developed an MCDM technique with bipolar fuzzy details. A comprehensive range of human decisions for selecting COVID-19 treatments is based on positive and negative double-sided or bipolar judgemental thinking. To select COVID-19 treatments in India, we have applied the proposed MCDM technique with BTrF information. Moreover, to demonstrate the applicability of our proposed MCDM method, we have considered a numerical example with BF data. Finally, we give the comparison study to show the effectiveness of our proposed MCDM method with other existing decision-making methods.

A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine

The development of information technology has had a significant impact on various areas of human activity, including medicine. It has led to the emergence of the phenomenon of Industry 4.0, which, in turn, led to the development of the concept of Medicine 4.0. Medicine 4.0, or smart medicine, can be considered as a structural association of such areas as AI-based medicine, telemedicine, and precision medicine. Each of these areas has its own characteristic data, along with the specifics of their processing and analysis. Nevertheless, at present, all these types of data must be processed simultaneously, in order to provide the most complete picture of the health of each individual patient. In this paper, after a brief analysis of the topic of medical data, a new classification method is proposed that allows the processing of the maximum number of data types. The specificity of this method is its use of a fuzzy classifier. The effectiveness of this method is confirmed by an analysis of the results from the classification of various types of data for medical applications and health problems. In this paper, as an illustration of the proposed method, a fuzzy decision tree has been used as the fuzzy classifier. The accuracy of the classification in terms of the proposed method, based on a fuzzy classifier, gives the best performance in comparison with crisp classifiers.