Secure Medical Diagnosis Using Rule Based Mining

2010 ◽  
pp. 34-42 ◽  
Author(s):  
M. A. Saleem Durai ◽  
N. Ch. Sriman Narayana Iyengar
Keyword(s):  
Medical Diagnosis ◽  
Rule Based

Rule-Based Systems for Medical Diagnosis

2015 ◽  
pp. 21-44
Author(s):  
V. S. Giridhar Akula
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Support System ◽  
Medical Diagnosis ◽  
Intelligent Systems ◽  
Inference Rules ◽  
Rule Based ◽  
Rule Based System ◽  
Fault Monitoring ◽  
Design Scheduling

A rule-based system is a set of “if-then” statements that uses a set of assertions, to which rules on how to act upon those assertions are created. Rule-based expert systems have played an important role in modern intelligent systems and their applications in strategic goal setting, planning, design, scheduling, fault monitoring, diagnosis, and so on. The theory of decision support system is explained in detail. This chapter explains how the concepts of fuzzy logic are used for forward and backward chaining. Patient data is analyzed with the help of inference rules.

Intrusion Detection Using Machine Learning

2010 ◽  
pp. 70-107
Author(s):  
Mohammed M. Mazid ◽  
A. B.M. Shawkat Ali ◽  
Kevin S. Tickle
Keyword(s):  
Intrusion Detection ◽  
Medical Diagnosis ◽  
Computer Network ◽  
Model Identification ◽  
Intrusion Detection Systems ◽  
Network Technology ◽  
Rule Based ◽  
Detection Systems ◽  
Research Areas ◽  
Future Direction

Intrusion detection has received enormous attention from the beginning of computer network technology. It is the task of detecting attacks against a network and its resources. To detect and counteract any unauthorized activity, it is desirable for network and system administrators to monitor the activities in their network. Over the last few years a number of intrusion detection systems have been developed and are in use for commercial and academic institutes. But still there have some challenges to be solved. This chapter will provide the review, demonstration and future direction on intrusion detection. The authors’ emphasis on Intrusion Detection is various kinds of rule based techniques. The research aims are also to summarize the effectiveness and limitation of intrusion detection technologies in the medical diagnosis, control and model identification in engineering, decision making in marketing and finance, web and text mining, and some other research areas.

scholarly journals An interpretable fuzzy rule-based classification methodology for medical diagnosis

2009 ◽  
Vol 47 (1) ◽  
pp. 25-41 ◽  
Author(s):  
Ioannis Gadaras ◽  
Ludmil Mikhailov
Keyword(s):  
Medical Diagnosis ◽  
Fuzzy Rule ◽  
Rule Based

An Interpretability-Accuracy Tradeoff in Learning Parameters of Intuitionistic Fuzzy Rule-Based Systems

2016 ◽  
Vol 20 (5) ◽  
pp. 773-787
Author(s):  
Yanni Wang ◽  
◽  
Yaping Dai ◽  
Yu-Wang Chen ◽  
Witold Pedrycz ◽  
...  
Keyword(s):  
Medical Diagnosis ◽  
Fuzzy Rule ◽  
Programming Method ◽  
Parameter Learning ◽  
Data Set ◽  
Rule Based ◽  
Intuitionistic Fuzzy ◽  
Objective Programming ◽  
Accuracy Tradeoff ◽  
Rule Based Systems

Parameter learning of Intuitionistic Fuzzy Rule-Based Systems (IFRBSs) is discussed and applied to medical diagnosis with intent of establishing a sound tradeoff between interpretability and accuracy. This study aims to improve the accuracy of IFRBSs without sacrificing its interpretability. This paper proposes an Objective Programming Method with an Interpretability-Accuracy tradeoff (OPMIA) to learn the parameters of IFRBSs by tuning the types of membership and non-membership functions and by adjusting adaptive factors and rule weights. The proposed method has been validated in the context of a medical diagnosis problem and a well-known publicly available auto-mpg data set. Furthermore, the proposed method is compared to Objective Programming Method not considering the interpretability (OPMNI) and Objective Programming Method based on Similarity Measure (OPMSM). The OPMIA helps achieve a sound a tradeoff between accuracy and interpretability and demonstrates its advantages over the other two methods.

Developing and Validating Fuzzy-Based Trust Measures for Online Medical Diagnosis and Symptoms Analysis

2015 ◽  
pp. 302-332 ◽  
Author(s):  
Shruti Kohli
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Medical Diagnosis ◽  
Medical Information ◽  
Literature Survey ◽  
Rule Based ◽  
Design And Implementation ◽  
Frequent Use ◽  
Soft Computing Techniques ◽  
The Web

The wealth of medical information in the Web makes it expedient for non-experts to conduct their own diagnosis and healthcare assessment based on limited knowledge of signs, symptoms, and disorders. The goal of this chapter is to explain how to measure trust of websites that provide functionalities like Online Medical Diagnosis and exploration of Symptoms Analysis using fuzzy logic and soft computing techniques. Trust is qualitative and can be measured by analyzing how people interact with the websites. The interaction can be captured and analyzed by studying website logs using tools like Google analytics, click tail, etc. The chapter also provides a literature survey on the work being conducted by researchers in area of measuring website trust and tools being developed for same. It also covers archetypal techniques used for Web pattern recognition and taxonomy of trust. The main point driven by this literature survey is the frequent use of fuzzy logic in the design and implementation of trust measuring tools. This point is contrasted with the up-to-the-minute information, more specifically the authors' current work, on the use of a rule-based expert for developing trust-measuring tools.

scholarly journals A novel weighted evidence combination rule based on improved entropy function with a diagnosis application

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882399 ◽  
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang
Keyword(s):  
Medical Diagnosis ◽  
Real Life ◽  
Entropy Function ◽  
New Method ◽  
Combination Rule ◽  
Initial Weight ◽  
Rule Based ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Shafer Theory

Managing conflict in Dempster–Shafer theory is a popular topic. In this article, we propose a novel weighted evidence combination rule based on improved entropy function. This newly proposed approach can be mainly divided into two steps. First, the initial weight will be determined on the basis of the distance of evidence. Then, this initial weight will be modified using improved entropy function. This new method converges faster when handling high conflicting evidences and greatly reduces uncertainty of decisions, which can be demonstrated by a numerical example where the belief degree is raised up to 0.9939 when five evidences are in conflict, an application in faulty diagnosis where belief degree is increased hugely from 0.8899 to 0.9416 when compared with our previous works, and a real-life medical diagnosis application where the uncertainty of decision is reduced to nearly 0 and the belief degree is raised up to 0.9989.

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