Computer aided fuzzy medical diagnosis

Centre for Computational Intelligence, Department of Computer Science, De Montfort University, The Gateway, Leicester LE1 9BH, UK
Inf. Sci 05/2004; 162(2):81-104. DOI: 10.1016/j.ins.2004.03.003
Source: DBLP


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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    • "Iakovidis and Papageorgio (2011) propose to use intuitionistic fuzzy cognitive maps to account for the hesitancy in the doctors' evaluation on the relationship between symptoms and possible diseases. Innocent and John (2004) developed a similar system using type-2 fuzzy sets and they found that accounting for this higher level of imprecision can improve the accuracy of diagnosis. "
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    ABSTRACT: Mobile applications and specifically wellness applications are used increasingly by different age-segments of the general population. This is facilitated by the large amount of data collected through various built-in sensors in the smartphone or other mobile devises, e.g. smart watches. Young-elderly cohort (60-75 year old individual) is probably one of the most potential user groups that would benefit from using mobile health and wellness applications, if their needs and preferences are precisely addressed. General knowledge is limited on understanding to what extent mobile wellness applications can and should provide precise recommendations which improve the users’ health and physical conditions. To address this problem, the current study identifies the potential benefits of utilizing fuzzy optimization tools to design recommendation systems that can take into consideration the (i) imprecision in the data and (ii) the imprecision by which one can estimate the effect of a recommendation on the user of the system. The proposed approach, depending on the context of use, identifies a set of actions to be taken by the users in order to optimize the physical or mental condition from various perspectives. The model is illustrated through the example of walking speed optimization which is an important issue for the young-elderly.
    Full-text · Conference Paper · Jun 2015
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    • "Although the usefulness of using neural networks and support machines has been reported in the literature, the obstacles are in model building and use of model in which the correlation between all the variables are difficulty to be inferred and understood. Recently, cognitive map [2] has been applied in many scientific areas, such as political science [3], organization and strategy planning [4] [5], analysis of business performance indicators [6], software project management [7], supply chain management [8]-[9], medical diagnosis [10]-[12], engineering design [13], etc. When a fuzzy cognitive map (FCM) has been constructed, it becomes a very usefully tool for supporting decision making [14]. "

    Preview · Article · Jan 2014
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    • "It accepted fuzzy descriptions of a patient's symptoms and inferred fuzzy descriptions of the patient's diseases by means of the fuzzy relationships. Furthermore, several researchers followed Sanchez's [27] approach to extend fuzzy inference to medical engineering, notable examples being Pavlica and Petrovacki [24], Steimann and Adlassnig [28], Innocent and John [15], Palma et al. [23], Seising [29], Quteishat and Lim [25], and Ahn et al. [1]. "
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    ABSTRACT: This paper provides an improved decision making approach based on fuzzy numbers and the compositional rule of inference by Yao and Yao (2001). They claimed to have created a new method that combines statistical methods and fuzzy theory for medical diagnosis. Currently, numerous papers have cited that work. In this study, we show that if we follow their matrix multiplication operation approach, we will obtain the same result as the original method proposed by Klir and Yuan (1995). Owing to a wellknown property of (row) stochastic matrices, if the multiplication is closed, the fuzzy and defuzzy procedure of Yao and Yao (2001) is redundant. Therefore, we advise researchers to think twice before applying this approach to medical diagnosis.
    Preview · Article · Jan 2014 · Yugoslav journal of operations research
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