Comparison of different strategies of utilizing fuzzy clustering in structure identification

Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then" rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification. Basically, there are three different approaches in which one can utilize fuzzy clustering; the �first one is based on input space clustering, the second one considers clustering realized in the output space, while the third one is concerned with clustering realized in the combined input-output space. In this study, we analyze these three approaches. We discuss each of the algorithms in great detail and o¤er a thorough comparative analysis. Finally, we compare the performances of these algorithms in a medical diagnosis classi�cation problem, namely Aachen Aphasia Test. The experiment and the results provide a valuable insight about the merits and the shortcomings of these three clustering approaches

Decision Making in the Medical Domain: Comparing the Effectiveness of GP-Generated Fuzzy Intelligent Structures

ABSTRACT: In this work, we examine the effectiveness of two intelligent models in medical domains. Namely, we apply grammar-guided genetic programming to produce fuzzy intelligent structures, such as fuzzy rule-based systems and fuzzy Petri nets, in medical data mining tasks. First, we use two context-free grammars to describe fuzzy rule-based systems and fuzzy Petri nets with genetic programming. Then, we apply cellular encoding in order to express the fuzzy Petri nets with arbitrary size and topology. The models are examined thoroughly in four real-world medical data sets. Results are presented in detail and the competitive advantages and drawbacks of the selected methodologies are discussed, in respect to the nature of each application domain. Conclusions are drawn on the effectiveness and efficiency of the presented approach

An intelligent system based on fuzzy probabilities for medical diagnosis - a study in aphasia diagnosis

Abstract BACKGROUND: Aphasia diagnosis is particularly challenging due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease

Fuzzy multiple criteria decision making approach in environmental risk assessment

Being able to evaluate risks is an important task in many areas of human activity: economics, ecology, etc. In case of a sufficient amount of source information the risk is evaluated using statistical methods. However, in reality the sufficiency of statistical data in risk assessment is more exceptional than normal. In such cases experts’ assessment make the only source of data. Experts are able to provide the necessary for analysis data due to their professional knowledge and experience. Certain amount of factors, which is to be evaluated by an expert (experts), significantly affects the process of experts’ assessment. If a big number of relevant factors occur, an expert may face a problem of defining links between “factors” and “outcome”. Fuzzy multiple criteria decision making approach can be used to solve the problem. Ecological risk assessment towards human health in case of gaseous substances escape at a chemical factory using hierarchical method and fuzzy multiple criteria decision making approach has been analyzed in the article

Improving the classification of multiple disorders with problem decomposition

AbstractDifferential diagnosis of multiple disorders is a challenging problem in clinical medicine. According to the divide-and-conquer principle, this problem can be handled more effectively through decomposing it into a number of simpler sub-problems, each solved separately. We demonstrate the advantages of this approach using abductive network classifiers on the 6-class standard dermatology dataset. Three problem decomposition scenarios are investigated, including class decomposition and two hierarchical approaches based on clinical practice and class separability properties. Two-stage classification schemes based on hierarchical decomposition boost the classification accuracy from 91% for the single-classifier monolithic approach to 99%, matching the theoretical upper limit reported in the literature for the accuracy of classifying the dataset. Such models are also simpler, achieving up to 47% reduction in the number of input variables required, thus reducing the cost and improving the convenience of performing the medical diagnostic tests required. Automatic selection of only relevant inputs by the simpler abductive network models synthesized provides greater insight into the diagnosis problem and the diagnostic value of various disease markers. The problem decomposition approach helps plan more efficient diagnostic tests and provides improved support for the decision-making process. Findings are compared with established guidelines of clinical practice, results of data analysis, and outcomes of previous informatics-based studies on the dataset

Speech and orofacial apraxias in Alzheimer's disease

Background: Alzheimer's disease (AD) affects not only memory but also other cognitive functions, such as orientation, language, praxis, attention, visual perception, or executive function. Most studies on oral communication in AD focus on aphasia; however, speech and orofacial apraxias are also present in these patients. the aim of this study was to investigate the presence of speech and orofacial apraxias in patients with AD with the hypothesis that apraxia severity is strongly correlated with disease severity.Methods: Ninety participants in different stages of AD (mild, moderate, and severe) underwent the following assessments: Clinical Dementia Rating, Mini-Mental State Examination, Lawton Instrumental Activities of Daily Living, a specific speech and orofacial praxis assessment, and the oral agility subtest of the Boston diagnostic aphasia examination.Results: the mean age was 80.2 +/- 7.2 years and 73% were women. Patients with AD had significantly lower scores than normal controls for speech praxis (mean difference = -2.9, 95% confidence interval (CI) = -3.3 to -2.4) and orofacial praxis (mean difference = -4.9, 95% CI = -5.4 to -4.3). Dementia severity was significantly associated with orofacial apraxia severity (moderate AD: beta = -19.63, p = 0.011; and severe AD: beta = -51.68, p < 0.001) and speech apraxia severity (moderate AD: beta = 7.07, p = 0.001; and severe AD: beta = 8.16, p < 0.001).Conclusion: Speech and orofacial apraxias were evident in patients with AD and became more pronounced with disease progression.Universidade Federal de São Paulo, Dept Speech Pathol, BR-04023900 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Neurol & Neurosurg, BR-04023900 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Prevent Med, BR-04023900 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Speech Pathol, BR-04023900 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Neurol & Neurosurg, BR-04023900 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Prevent Med, BR-04023900 São Paulo, BrazilWeb of Scienc

Переживание личного времени при болезни Альцгеймера

Original manuscript received February 3, 2020.Revised manuscript accepted March 18, 2020.Background. Alzheimer’s Disease (AD) is a neurodegenerative illness, which occurs with increasing frequency as people age, and is currently a disease beyond curative therapeutic possibilities. While progressive memory impairment is the upfront element associated with the disease, other neurocognitive problems are also associated with it, such as language impairment that tend to degenerate into aphasia. The paper presents singular representations of time in such patients’ lived experience, which with the course of the disease gradually move away from the real present around them. In consequence, they live in a bygone era. The dimension of the perceived world implies an anchoring in temporality in the present by using the resources of the past. This becomes impossible with the progression of the cognitive disorders. Multiple psychological tensions result from patient’s maladjustment to temporality. Objective. The article attempts to define the singular experience of the time of the demented patient according to the phases of the disease. Design. An analysis of the psychological tensions summoned at the various stages of the disease was carried out using a semiotic methodology. Results. The progression of the disease gives way to another mode of relation to the world, presentism. With its course, emotions can no longer unfold. At first, the patient seeks to increase his grip on the surrounding world, and the internal psychological tensions are accentuated. Suddenly the world ends up completely escaping him, and catastrophically the internal tensions collapse. The patient slides irreversibly towards regression. Conclusion. If the emotional intensity of what the patient experiences is enough, he remains engaged in the time of a presence in the world, even if it is distinct from the reality of those around him. When the intensity collapses, the demented patients disengage from it. The caregivers or families who take care of them can understand the mechanisms involved in the patients’ experience of time. This allows them to adapt their own behavior. Thus, they limit the risk of misinterpretation and induce certain behavioral disorders linked to their shift from the reality of the patients.Краткое введение. Болезнь Альцгеймера (AD) — это нейродегенеративное заболевание, случающееся с возрастающей частотой при увеличении возраста, и в настоящее время находится за рамками лечебных терапевтических возможностей. Первым признаком болезни является прогрессирующее ухудшение памяти, но при AD возникают и другие нейрокогнитивные проблемы, например, речевое расстройство, имеющее тенденцию переходить в афазию. В статье описаны единичные представления времени в жизненном опыте пациентов с болезнью Альцгеймера, которые с течением болезни постепенно отдаляются от реального настоящего вокруг них. Фактически они живут в давно ушедшей эпохе. Измерение воспринимаемого мира подразумевает привязку к временнóму в настоящем посредством использования ресурсов прошлого. Это становится невозможным при прогрессировании когнитивных расстройств. Многочисленные психологические напряжения возникают в результате неправильного приспособления пациента к темпоральности. Цель. В статье предпринята попытка определить единичный опыт переживания времени у больных с деменцией в соответствии с фазами заболевания. Дизайн. Анализ психологических напряжений, вызванных на различных стадиях заболевания, проводился с использованием семиотической методологии. Результаты. Прогрессирование болезни вызывает другой способ отношения к миру, презентизм. При этом больной уже не может выражать свои эмоции. Сначала пациент стремится усилить свою “хватку” за окружающий мир, и внутреннее психологическое напряжение также усиливается. Но внезапно мир полностью ускользает от него, и внутреннее напряжение катастрофически разрушается. Пациент необратимо скатывается к регрессии. Вывод. Если эмоциональная интенсивность переживаний пациента достаточна, он остается вовлеченным в жизнь, хотя его восприятие и отличается от реальности окружающих его людей. Когда интенсивность эмоций резко снижается, пациенты с деменцией выпадают из реальности. Ухаживающий персонал или семьи, которые заботятся о больных, могут понять механизмы, задействованные в переживании пациентами времени. Это позволяет им адаптировать свое собственное поведение. Таким образом, уменьшается риск неправильного толкования поведения больных и можно предупредить определенные поведенческие расстройства у пациентов

Exploring the Diagnosis of Frontotemporal Dementia by Analyzing Neuropsychological Data With K-Means Clustering

Background: Differential diagnosis of dementia syndromes is difficult. Treatments are available for Alzheimer’s dementia (AD), but effects do not translate to other dementia syndromes. Therefore, early differential diagnosis is necessary to administer appropriate interventions and to boost drug development and research of therapeutic strategies that may lower patient and care-giver burden. The focus of the current study is to disentangle the clinical picture of frontotemporal dementia (FTD) from AD spectrum disorders. Findings could support an early, cheap, and more accurate diagnosis. Methods: K-means clustering, an unsupervised machine learning algorithm, was used on neuropsychological data from neurologic patients of the FTLD consortium databank. The analysis was performed twice, once including only neuropsychological test scores and a second time combining the neuropsychological variables with questionnaire scores assessing behavioral changes. In total n = 484 and n = 469 participants were included in the analysis with and without questionnaires, respectively. Participants included were either healthy controls with no family relation to patients in the dataset, or patients diagnosed with one of the following dementia disorders: AD, a behavioral variant of FTD (bvFTD), or one of three possible primary progressive aphasia (PPA) syndromes - a semantic variant (svPPA), a non-fluent variant (nfvPPA) or a logopenic variant (lvPPA). Results: Agreement of results from the various analyses performed was relatively high. Homogeneous clusters of diagnostic groups emerged. Homogeneity seemed higher for bvFTD and svPPA than for the other patient groups. NfvPPA and lvPPA patients were particularly likely to cluster together. Exploring neuropsychological patterns of cluster results demonstrated high variability between patients of the same diagnostic groups which could partly be explained by differences in disease severity. Tests that might prove particularly relevant to distinguish diagnostic subgroups are the FTLD-CDR sub-scores, the repeat and point task as well as questionnaires assessing apathy. Conclusion: K-means clustering proved to be a useful technique to explore various diagnostic syndromes that show overlapping clinical pictures. This study helped to formulate specific hypotheses based on the observation of patterns in multidimensional data. Disease severity showed to impact k-means clustering results considerably and should therefore be accounted for in future studies. Future studies will need to test the formulated hypotheses and inspect the meaning of impure clusters. Keywords: k-means clustering, frontotemporal dementia, primary progressive aphasia, Alzheimer’s dementia, differential diagnosis, neuropsychological testsBackground: Differential diagnosis of dementia syndromes is difficult. Treatments are available for Alzheimer’s dementia (AD), but effects do not translate to other dementia syndromes. Therefore, early differential diagnosis is necessary to administer appropriate interventions and to boost drug development and research of therapeutic strategies that may lower patient and care-giver burden. The focus of the current study is to disentangle the clinical picture of frontotemporal dementia (FTD) from AD spectrum disorders. Findings could support an early, cheap, and more accurate diagnosis. Methods: K-means clustering, an unsupervised machine learning algorithm, was used on neuropsychological data from neurologic patients of the FTLD consortium databank. The analysis was performed twice, once including only neuropsychological test scores and a second time combining the neuropsychological variables with questionnaire scores assessing behavioral changes. In total n = 484 and n = 469 participants were included in the analysis with and without questionnaires, respectively. Participants included were either healthy controls with no family relation to patients in the dataset, or patients diagnosed with one of the following dementia disorders: AD, a behavioral variant of FTD (bvFTD), or one of three possible primary progressive aphasia (PPA) syndromes - a semantic variant (svPPA), a non-fluent variant (nfvPPA) or a logopenic variant (lvPPA). Results: Agreement of results from the various analyses performed was relatively high. Homogeneous clusters of diagnostic groups emerged. Homogeneity seemed higher for bvFTD and svPPA than for the other patient groups. NfvPPA and lvPPA patients were particularly likely to cluster together. Exploring neuropsychological patterns of cluster results demonstrated high variability between patients of the same diagnostic groups which could partly be explained by differences in disease severity. Tests that might prove particularly relevant to distinguish diagnostic subgroups are the FTLD-CDR sub-scores, the repeat and point task as well as questionnaires assessing apathy. Conclusion: K-means clustering proved to be a useful technique to explore various diagnostic syndromes that show overlapping clinical pictures. This study helped to formulate specific hypotheses based on the observation of patterns in multidimensional data. Disease severity showed to impact k-means clustering results considerably and should therefore be accounted for in future studies. Future studies will need to test the formulated hypotheses and inspect the meaning of impure clusters. Keywords: k-means clustering, frontotemporal dementia, primary progressive aphasia, Alzheimer’s dementia, differential diagnosis, neuropsychological test

A Genetic Tuning to Improve the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets: Degree of Ignorance and Lateral Position

Fuzzy Rule-Based Systems are appropriate tools to deal with classification problems due to their good properties. However, they can suffer a lack of system accuracy as a result of the uncertainty inherent in the definition of the membership functions and the limitation of the homogeneous distribution of the linguistic labels. The aim of the paper is to improve the performance of Fuzzy Rule-Based Classification Systems by means of the Theory of Interval-Valued Fuzzy Sets and a post-processing genetic tuning step. In order to build the Interval-Valued Fuzzy Sets we define a new function called weak ignorance for modeling the uncertainty associated with the definition of the membership functions. Next, we adapt the fuzzy partitions to the problem in an optimal way through a cooperative evolutionary tuning in which we handle both the degree of ignorance and the lateral position (based on the 2-tuples fuzzy linguistic representation) of the linguistic labels. The experimental study is carried out over a large collection of data-sets and it is supported by a statistical analysis. Our results show empirically that the use of our methodology outperforms the initial Fuzzy Rule-Based Classification System. The application of our cooperative tuning enhances the results provided by the use of the isolated tuning approaches and also improves the behavior of the genetic tuning based on the 3-tuples fuzzy linguistic representation.Spanish Government TIN2008-06681-C06-01 TIN2010-1505