Prediction of Severity of Diabetes Mellitus using Fuzzy Cognitive Maps

The objective to develop this research paper is concerned with a system which helps diagnose the severity of diabetes. The disease named diabetes mellitus makes the body unable to handle sugar so it causes thirst, frequency of urination, tiredness and many other symptoms. The diabetes mellitus describes a metabolic disorder characterized by chronic hyperglycemia with disturbances of carbohydrate, fat and protein metabolism resulting from defects in insulin secretion, insulin action, or both. It can be caused by number of factors like pancreatic dysfunction, obesity, hereditary, stress, drugs, alcohol etc. It includes long term damage, dysfunction and failure of various organs. The effects of diabetes mellitus include long term damage and failure of various organs. Diabetes mellitus may present with characteristic symptoms such as thirst, polyuria, blurring of vision, and weight loss. This Paper is implemented on soft computing technique, namely Fuzzy Cognitive Maps (FCM) to find out the presence or absence of diabetes mellitus based on the input of sign/symptoms recorded at three fuzzy levels developed by the domain experts. The large amount of data and information that needs to be handled and integrated requires specific methodologies and tools. The FCM based decision support system was developed with a view to help medical and nursing personnel to assess patient status assist in making a diagnosis. The software tool was tested on 50 cases, showing results with an accuracy of 96%. The analysis of experimental results of different applicants checks the correctness and consistency of decision Support system for correct decision making. Keywords: Fuzzy Logic, FCM, Diabetes Mellitus, Prediction, Symptoms

Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps

As extension of Fuzzy Cognitive Maps are now introduced the Neutrosophic Cognitive Map

Cognitive Maps

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The Use of Clinical Judgment in Differentiating Symptoms of Autism Spectrum Disorder from Those of Other Childhood Conditions: A Delphi Study

More and more, due to long waiting lists at diagnostic clinics and access barriers for certain segments of the population, schools are often the first environment in which children are evaluated for ASD (Sullivan, 2013). And while accurate identification of autism spectrum disorders (ASD) is essential for proper treatment and service provision, large percentages of school and community-based identifications of ASD are overturned when children are re-evaluated with strict clinical criteria (Wiggins et al., 2015). In part, challenges faced in accurately differentiating ASD from other conditions may be contributed to the diagnostic complexities of the condition itself. Clinical expertise is one of, if not the most important factors in accurate diagnostic decision-making during evaluations of ASD. However, there exists little insight into what comprises this expert judgment. Using the Delphi methodology, a panel of clinical and school psychology experts in ASD identification were surveyed until consensus was reached about their use of clinical judgment in differentiating ASD from other conditions. The results of these rounds of questioning were compiled into a decision-making guideline entitled Beyond Test Results: Developing Clinical Judgment to Differentiate Symptoms of Autism Spectrum Disorders from Those of Other Childhood Conditions. Implications of this guide include incorporation into school psychology training courses and guidance for school-based evaluation teams

A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features

Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations

Financial management of small organizations based on a cognitive approach

In most cases small business organizations insufficiently justify economic calculations for the formation of financial resources, which adversely affects their sustainable development. Under the current circumstances, the need for sound financial management increases, which ensures a stable financial status of the organization and the prospects for increasing its value. The aim of this paper is to identify the conditions for the dynamic financial management of small organizations based on a cognitive approach. The proposed cognitive model allows establishing the mechanism of mutual influential factors of internal and external environment on the effective use of financial resources. Using the proposed model, it is possible to forecast changes in financial results.peer-reviewe