Computer aided fuzzy medical diagnosis.
ABSTRACT This paper describes a fuzzy approach to computer aided medical diagnosis in a clinical context. It introduces a formal view of diagnosis in clinical settings and shows the relevance and possible uses of fuzzy cognitive maps and fuzzy logic. A constraint satisfaction method is introduced which uses the temporal uncertainty in symptom durations that may occur with particular diseases. Together with fuzzy symptom descriptions, the method results in an estimate of the stage of a disease if the temporal constraints of the disease in relation to the occurrence of the symptoms are satisfied. The approach is evaluated through simulation experiments showing the effects of symptom ordering, temporal uncertainty and symptom strengths on the diagnosis efficiency. The method is effective and can be developed further using second order (Type 2) fuzzy logic to better represent uncertainty in the clinical context thus improving differential diagnosis accuracy.
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ABSTRACT: The paper proposes an approach to support human abductive reasoning in the diagnosis of a multiviewpoint system. The novelty of this work lies on the capability of the approach to treat the uncertainty held by the agent performing the diagnosis. To do so, we make use of evidential networks to represent and propagate the uncertain evidence gathered by the agent. Using forward and backward propagation of the information, the impact of the evidence over the different symptoms and causes of failure is quantified. The agent can then make use of this information as additional hints in an iterative diagnosis process until a desired degree of certainty is obtained. The model is compared with a deterministic one in which evidence is represented by binary states, that is, a symptom is either observed or not.Information Sciences 12/2013; 253:110-125. DOI:10.1016/j.ins.2013.07.014 · 3.89 Impact Factor
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ABSTRACT: One of the toughest challenges in medical diagnosis is the handling of uncertainty. Since medical diagnosis with respect to the symptoms uncertain, they will be assumed to have an intuitive nature. Thus, to obtain the uncertain optimism degree of the doctor, fuzzy linguistic quantifiers will be used. The aim of this article is to provide an improved nonprobabilistic entropy approach to support doctors examining the work of the preliminary diagnosing. The proposed entropy measure is based on intuitionistic fuzzy sets, extrainformation regarding hesitation degree, and an intuitive and mathematical connection between the notions of entropy in terms of fuzziness and intuitionism has been revealed. An illustrative example for medical pattern recognition demonstrates the usefulness of this study. Furthermore, in order to make computing and ranking results easier and to increase the recruiting productivity, a computer-based interface system has been developed to support doctors in making more efficient judgments.Advances in Fuzzy Systems 01/2012; 2012(1687-7101). DOI:10.1155/2012/863549
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ABSTRACT: Abstract: Malaria represents major public health problems in the tropics. The harmful effects of malaria parasites to the human body cannot be underestimated. In this paper, a fuzzy expert system for the management of malaria (FESMM) was presented for providing decision support platform to malaria researchers, physicians and other healthcare practitioners in malaria endemic regions. The developed FESMM composed of four components which include the Knowledge base, the Fuzzification, the Inference engine and Defuzzification components. The fuzzy inference method employed in this research is the Root Sum Square (RSS). The Root Sum Square of drawing inference was employed to infer the data from the fuzzy rules developed. Triangular membership function was used to show the degree of participation of each input parameter and the defuzzification technique employed in this research is the Centre of Gravity (CoG). The fuzzy expert system was designed based on clinical observations, medical diagnosis and the expert’s knowledge. We selected 35 patients with malaria and computed the results that were in the range of predefined limit by the domain experts. Keywords: Malaria, Fuzzy Logic, Knowledge base, Medical Diagnosis, Fuzzy Expert System