Studies on Multi-Criteria Decision-Making Based Healthcare Systems Using The Machine Learning

Abstract

There is a lot of information in healthcare and medical records. However, it is challenging for humans to turn data into information and spot hidden patterns in today's digitally-based culture. Effective decision-support technologies can help medical professionals find critical information concealed in voluminous data and support their clinical judgments as well as in different healthcare management activities. Research paper presented an extensive literature survey for Healthcare systems using the machine learning that is based on Multiple criteria decision making (MCDM). Various existing studies are considered for review and a critical analysis is being done through the reviews study which can help to the researchers to explore further research area to cater the need of the field

Improving the Trustworthiness of Interactive Visualization Tools for Healthcare Data through a Medical Fuzzy Expert System

“…In addition, the evaluation of the trustworthiness of the interactive visualization achieved through fuzzy decision making in healthcare for medical diagnosis purposes is an ongoing process that is required to be carried out on a regular basis by industry professionals in order to test the security, effectiveness, and accuracy of these products. As a result, the goal of this research endeavor on our part is to evaluate the trustworthiness of interactive visualization achieved by means of fuzzy decision making in the medical field and to carry out an idealness evaluation while taking into account the constraints imposed by multi-criteria decision-making (MCDM) methodologies, since this problem is divided into different criteria [15,16]. For the purpose of this evaluation, the identification and selection of the pertinent characteristics are determined by the opinions of the specialists, which are provided in the second section of this paper.…”

An MCDM Approach for Enhancing Breast Cancer Diagnosis Using AHP and Bidirectional RNN with Bayesian Optimization

“…Breast cancer is caused by a number of different factors, some of which are radiation therapy, hormones, obesity, genetics, and family history [1]. Reproductive factors also play a role.…”