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Methodology for Medical Diagnosis based on Fuzzy Logic.
... As multiple levels of imprecisions and uncertainty is involved in medical field. Fuzzy logic concepts can play an important role in the diagnosis of an ailment (Madkour and Roushdy 2004). The authors of Srivastava et al. (2016) have presented a psychological analysis of adolescents through fuzzy logic. ...
The role of a healthcare practitioner is to diagnose a disease and find an optimum means for suitable treatment. This has been the most challenging task over the years. The researchers have been developing intelligent tools for providing support in taking medical decision. The application of AI in different health scenario strengthen the mechanism for finding a better treatment plan. The authors share some recent advancements in this domain. The role of artificial intelligence in Indian healthcare system has also been discussed. The paper analyzes the use of different AI techniques like fuzzy logic, Artificial Neural Networks, Particle Swarm Optimization and Fuzzy Neural in critical health scenario. A detail literature review has been provided in this context. The disease taken into consideration are cancer, TB, diabetes, malaria, orthopedics, obesity and disease specific to elderly people. The purpose of this article is to find the relevance of various techniques of AI in different critical health scenarios. A comparative analysis is done based on the publications since 1995. The challenges and risks associated with the usage of AI in healthcare has been analysed and suggestions made for making the analysis in the health domain more accurate and effective. Further the concept of deep learning has also been explained and its inculcation with the medical domain is discussed.
In the article was discussed the methods (decision trees, deep learning algorithms, k-nearest neighbors, neural networks) to create diagnostic expert medical systems. For practice part were developed diagnostic API based on chosen classifiers that implement the algorithms and a study of their work was conducted. Namely, classifiers based on neural networks, decision trees and k-nearest neighbors method were compared. The parameters for the selected classifier were optimized. As a result, were selected parameters on which the data were researched. In addition, the dataset of information of patients who had heart attack was researched to develop a diagnostic system for revealing heart diseases. The diagnostic API for revealing patients’ heart diseases is described. Keywords: diagnostic systems, medical systems, neural networks, decision trees, diagnostic API.
The chapter introduces the history of the clinical decision support system beginning with the history of the system of decision making. It is an overview of how the clinical decision support systems developed through the years. The current technology used in decision making are also discussed. With the use of artificial intelligence, the clinical decision support systems have moved to the realm of predictive analysis to find out the possibilities of diseases rather than just the diagnosis and treatment. The chapter also elaborates the various types of clinical decision supports systems. Although the decision support systems are widely regard as an important and integral part of healthcare there has been a notable reluctance in the use of clinical decision support systems. The chapter also discusses the practical challenges in the implementation of the clinical decision support systems in healthcare organisations. Each of the topics in the chapter is dealt with summarily and the reference to a detailed study is provide. The idea is to provide a clear understanding of the system rather than to fully elaborate the system.
The increasing prevalence of complex technology in the form of medical expert systems in the healthcare sector is presenting challenging opportunities to clinicians in their quest to improve patients’ health outcomes. Medical expert systems have brought measurable improvements to the healthcare outcomes for some patients. This paper highlights the importance of trust and acceptance in the healthcare industry amongst receivers of the care as well as other stakeholders and between large healthcare organizations. Studies show that current conceptual trust models, which are being used to measure the degree of trust relationships in different healthcare settings, cannot be easily evaluated because of the resistance of organizational and social changes which are to be implemented. Research findings also suggest that the use of medical expert systems do not automatically guarantee improved patient healthcare outcomes. Furthermore, during the building of predictive and diagnostic expert medical systems, studies recommend the use of algorithms which can deal with noisy and imprecise data which is typical in healthcare data. Such algorithms include fuzzy rule based systems.
The process of medical diagnosis, like many other fields, has to pass through various stages of uncertainty, especially in cases where the data is mostly available in linguistic format. Under such circumstances of vague data, application of fuzzy logic concepts can play an important role in extracting approximate information which in turn may help in reaching to a particular diagnosis. The present study is devoted to the application of fuzzy logic rules for analyzing the psychology of adolescents with respect to Indian scenario. The objective here is to identify whether the subject requires counselling. Fuzzy logic approach is applied to Global Adjustment Scale, used by mental health clinicians to rate the general functioning of children under the age of 18.
This chapter is devoted to the methodology aspects of identification and decision making on the basis of intellectual technologies. The essence of intellectuality consists of representation of the structure of the object in the form of linguistic IF-THEN rules, reflecting human reasoning on the common sense and practical knowledge level. The linguistic approach to designing complex systems based on linguistically described models was originally initiated by Zadeh [1] and developed further by Tong [2], Gupta [3], Pedrych [4 – 6], Sugeno [7], Yager [8], Zimmermann [9], Kacprzyk [10], Kandel [11]. The main principles of fuzzy modeling were formulated by Yager [8]. The linguistic model is a knowledge-based system. The set of fuzzy IF-THEN rules takes the place of the usual set of equations used to characterize a system [12 – 14]. The fuzzy sets associated with input and output variables are the parameters of the linguistic model [15]; the number of the rules determines its structure. Different interpretations of the knowledge contained in these rules, which are due to different reasoning mechanisms, result in different types of models.
The process of medical diagnosis, like many other fields, has to pass through various stages of uncertainty, especially in cases where the data is mostly available in linguistic format. Under such circumstances of vague data, application of fuzzy logic concepts can play an important role in extracting approximate information which in turn may help in reaching to a particular diagnosis. This study is devoted to the application of fuzzy logic in the psychological domain. The paper provides a detailed literature review on the use of fuzzy logic rules in analyzing the different aspects of psychological behavior of human beings. Further, it also provides some suggestions to make the system more effective.
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