Rule-Based Systems for Medical Diagnosis

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.

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

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.

Fuzzy Expert System to Diagnose Diabetes Using S Weights for S Fuzzy Assessment Methodology

In the field of medicine decision making it is very important to deal with uncertainties, knowledge, and information. Decision making depends upon the experience, capability, and the observation of doctors. In the case of complex situations, it is very tough to give a correct decision. So computer-based procedure is very much essential. Fuzzy Expert System is used for decision making in the field of medicine. Fuzzy expert system consists of the following elements, fuzzification interface, S Fuzzy Assessment Methodology, and defuzzification. S Fuzzy Assessment Methodology uses the K Ratio to find overlap between membership function. To measure the similarity between fuzzy set, fuzzy number, and fuzzy rule, T Fuzzy similarity is used. Similar fuzzy sets are merged to form a common set; a new methodology was framed to identify the similarity between fuzzy rules with fuzzy numbers, and S Weights are to manage uncertainty in rules. S Weights use consequent and antecedent part of each rule. The efficiency of the proposed algorithm was implemented using MATLAB Fuzzy Logic tool box to construct a fuzzy expert system to diagnose diabetes.

Diagnosis of Angina Using Neuro-Fuzzy Technique

The majority of people will suffer heart attacks (Dr. A. Abdul Jallel, Personal communication, December 10, 2005). To confirm the disease, patients need to undergo tests like ECG, treadmill, etc., which cost money and time. After heavy expenses and large duration only, they come to know whether they are affected by a heart attack or another disease. To help the people, it is planned to give a suitable solution by an emerging technology called “Neuro-Fuzzy.” The main objective of this chapter is to identify the symptom called “angina,” which is severe chest pain prior to a heart attack. With various symptoms like location of pain, radiation of pain, precipitation of pain, duration of pain, cholesterol, smoking, family history, angina can be diagnosed. After the diagnosis, it is possible to know angina's severity, and based on it, a biopsy is recommended for the affected people.

Knowledge Representation Using Fuzzy XML Rules in Web-Based Expert System for Medical Diagnosis

The Web is a huge repository of information for large spectrum of decision making and advise. To effectively utilise it, there is a need for knowledge-based techniques. This chapter proposes a novel technique of knowledge representation using a fuzzy eXtensible Markup Language (XML). XML is an efficient tool to represent content; however, it lacks management of uncertainty and vagueness. The proposed technique serves dual advantages such as making the application Web-enabled and imparting benefits of uncertainty and intelligence. This chapter presents the general structure of fuzzy XML rule, DTD model, and the generic architecture of Web-based expert systems using fuzzy XML knowledge base for a variety of applications in different areas. To demonstrate the architecture proposed, an abdomen pain diagnosing system for appendicitis is discussed with sample rules along with a decision tree for the case.

Hybrid Approach for Analyzing Acute Spots of Clinical Speech Data Using Fuzzy Inference System

Acoustical measures of vocal functions are used in the assessments of voice disorders and monitoring the subject's improvement with speech therapy. In this chapter, a hybrid approach is proposed to identify the acute spots in pathological speech signals. These spots represents where the speech disorder occurs. The speech training for that specific portion of speech in particular could be given for enhancing the speeches. Dimensionality reduction is done using Principal Component Analysis (PCA) on Mel Frequency Cepstrum Coefficients (MFCC) extracted. By statistical method it is proved that overall 91.60% of the words were classified correctly. The features were trained using Support Vector Machines (SVM) for categorizing normally and abnormally pronounced words. The peaks found by Fast Fourier Transform (FFT) in abnormal words is made use of in the Fuzzy Inference System (FIS) for finding the acute spots in which the aberration has occurred in the word. This hybrid approach was found to have around 98% accuracy.

Application of Fuzzy Expert System in Medical Treatment

The incidence of breast cancer is increasing day by day. Emotional significance of females for the fear of removal of breast demands attention and carries a particular terror. Fuzzy logic-based expert system is a powerful tool that is used in this chapter to get the benefits of soft computing in modern medical science. This chapter deals with reasoning for medical implementation in breast cancer diagnosis. The motto of this expert system using MATLAB software is to make the people of the world healthier, free from breast cancer and its metastasis through the power of information. Special revolutionary screening technology and a few (cancer markers) blood tests enable breast cancer diagnosis, configuration, and control, and prompt necessary decisions for treatment. Thus, this system provides healthier living, better healthcare outcomes, and helps to lower the overall cost of the healthcare system.

Fuzzy Logic for Breast Cancer Diagnosis Using Medical Thermogram Images

In this chapter, how medical thermography can be utilized as early detection technique for breast cancer with fuzzy logic is explained. Breast cancer is the leading cause of death among women. This fact justifies researches to reach early diagnosis, improving patients' life expectancies. Moreover, there are other pathologies, such as cysts and benign neoplasms, that deserve investigation. In the last ten years, the infrared thermography has shown to be a promising technique to early diagnosis of breast pathologies. Works on this subject presented results that justify the thermography as a complementary exam to detect breast diseases. Various algorithms that can be utilized for Breast Cancer diagnosis utilizing medical thermography are listed and also the advantages of medical thermography over other imaging modalities is given.

Implementation of Fuzzy Technology in Complicated Medical Diagnostics and Further Decision

This chapter presents the importance of fuzzy expert systems in the medical field. Efficient and suitable medical work becomes difficult many times without the knowledge of the rules of logic. The chapter highlights the ways of implementing both classical logic and non-classical approach (e.g. temporal and fuzzy logic) in some adverse areas of medical diagnostics. The implementation of fuzzy expert systems is supported by some examples illustrating how indispensable the cognition of logic and showing how applying logic can effectively improve work in medicine. Fuzzy Expert Systems for diagnosis of urinary incontinence, Parkinson's disease, including neurological signs in domestic animals, as well as its implementation for diagnosis of prostate cancer are elaborately discussed.

Fuzzy-Based Medical Image Processing

Medical Image Processing (MIP) is a set of tools applied over medical images, which consists of several components such as image acquisition, enhancement, segmentation, restoration, etc. The most important component of MIP is medical image segmentation used in Computer-Aided Diagnosis (CAD) systems used for detection of abnormalities in medical images. This chapter presents an overview and the importance of soft computing techniques in solving the problems of medical imaging. The authors highlight the significance of fuzzy-based clustering and similar methods for MIP and its applications. Fuzzy C-Means Clustering Method (FCM) is found the most suitable method among existing clustering methods for medical images. FCM addresses the problem of over-segmentation and helps in improvement of diagnosis accuracy. Application of optimization tool causes the reduction of execution time. A comparison of fuzzy-based methods over conventional methods suggests that neuro-fuzzy system as hybrid approach is an efficient method for medical image analysis.