Clinical Characteristics of Subependymal Giant Cell Astrocytoma in Tuberous Sclerosis Complex

BACKGROUND: This study evaluated the characteristics of subependymal giant cell astrocytoma (SEGA) in patients with tuberous sclerosis complex (TSC) entered into the TuberOus SClerosis registry to increase disease Awareness (TOSCA). METHODS: The study was conducted at 170 sites across 31 countries. Data from patients of any age with a documented clinical visit for TSC in the 12 months preceding enrollment or those newly diagnosed with TSC were entered. RESULTS: SEGA were reported in 554 of 2,216 patients (25%). Median age at diagnosis of SEGA was 8 years (range, 18 years. SEGA were symptomatic in 42.1% of patients. Symptoms included increased seizure frequency (15.8%), behavioural disturbance (11.9%), and regression/loss of cognitive skills (9.9%), in addition to those typically associated with increased intracranial pressure. SEGA were significantly more frequent in patients with TSC2 compared to TSC1 variants (33.7 vs. 13.2 %, p < 0.0001). Main treatment modalities included surgery (59.6%) and mammalian target of rapamycin (mTOR) inhibitors (49%). CONCLUSIONS: Although SEGA diagnosis and growth typically occurs during childhood, SEGA can occur and grow in both infants and adults

On the Logic of Left-Continuous t-Norms and Right-Continuous t-Conorms

Double residuated lattices are expansions of residuated lattices with an extra monoidal operator, playing the role of a strong disjunction operation, together with its dual residuum. They were introduced by Orlowska and Radzikowska. In this paper we consider the subclass of double residuated structures that are expansions of MTLalgebras, that is, prelinear, bounded, commutative and integral residuated lattices. MTL-algebras constitute the algebraic semantics for the MTL logic, the system of mathematical fuzzy logic that is complete w.r.t. the class of residuated lattices on the real unit interval [0, 1] induced by left-continuous t-norms. Our aim is to axiomatise the logic whose intended semantics are commutative and integral double residuated structures on [0, 1], that are induced by an arbitrary left-continuous t-norm, an arbitrary right-continuous t-conorm, and their corresponding residual operations

A fuzzy-rough approach to the representation of linguistic hedges

We present a new representation for linguistic hedges using a framework of fuzzy rough sets. In traditional fuzzy-set theoretical representations, properties of objects such as old and experienced, axe represented by a fuzzy set P, while linguistic hedges (i.e. expressions like very, more or less, rather) are modelled by means of some transformations applied to P. In contrast to these approaches, we propose a representation which allows us to express the meaning of a statement like "x is very P" also relative to mutual resemblances between objects in the domain of discourse. This allows for adequate context-dependent characteristics of objects. Technically, this is achieved by using fuzzy rough approximators with respect to fuzzy resemblance relations representing mutual resemblances between objects. We show that this framework allows for flexible representation of some linguistic terms

Semi-Supervised Fuzzy-Rough Feature Selection

With the continued and relentless growth in dataset sizes in recent times, feature or attribute selection has become a necessary step in tackling the resultant intractability. Indeed, as the number of dimensions increases, the number of corresponding data instances required in order to generate accurate models increases exponentially. Fuzzy-rough set-based feature selection techniques offer great flexibility when dealing with real-valued and noisy data; however, most of the current approaches focus on the supervised domain where the data object labels are known. Very little work has been carried out using fuzzy-rough sets in the areas of unsupervised or semi-supervised learning. This paper proposes a novel approach for semi-supervised fuzzy-rough feature selection where the object labels in the data may only be partially present. The approach also has the appealing property that any generated subsets are also valid (super)reducts when the whole dataset is labelled. The experimental evaluation demonstrates that the proposed approach can generate stable and valid subsets even when up to 90% of the data object labels are missing

Fuzzy Logic and Correlation-Based Hybrid Classification on Hepatitis Disease Data Set

International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME) -- APR 20-22, 2019 -- Antalya, TURKEYDevelopments in the health field are closely affecting humanity. The development of information technologies increases this effect. In this study, it was aimed to help the decision makers by increasing the accuracy rate in the detection of hepatitis disease. The data set was obtained from UCI machine learning source. Data preprocessing, attribute selection and classifier models were established on this data set, respectively. After the deficiency in the data of the patients with hepatitis was normalized, correlation-based and fuzzy-based rough force attribute selection methods were applied and the attributes that contributed to the classification were selected. The hepatitis dataset and the data set formed by the attributes determined by the correlation-based and the fuzzy-based rough-attribute selection methods were classified using the k-nearest neighbor, Random Forest, Naive Bayes, and Logistic Regression algorithms and the results were compared. Accuracy, sensitivity precision, ROC curve and F-measure values were used in the comparison of classification algorithms. In the process of separating the data set as a test and training set, a 5-fold cross-validation method was applied. It has been observed that the fuzzy rough clustering algorithm is more successful than the k-nearest neighbor, Random Forest, Naive Bayes, and Logistic Regression classification methods in the detection of hepatitis disease.WOS:0006787710000682-s2.0-8508345020

Third Order Backward Elimination Approach for Fuzzy-Rough Set Based Feature Selection

Two important control strategies for Rough Set based reduct computation are Sequential Forward Selection (SFS), and Sequential Backward Elimination (SBE). SBE methods have an inherent advantage of resulting in reduct whereas SFS approaches usually result in superset of reduct. The fuzzy rough sets is an extension of rough sets used for reduct computation in Hybrid Decision Systems. The SBE based fuzzy rough reduct computation has not attempted till date by researchers due to the fuzzy similarity relation of a set of attributes will not typically lead to fuzzy similarity relation of the subset of attributes. This paper proposes a novel SBE approach based on Gaussian Kernel-based fuzzy rough set reduct computation. The complexity of the proposed approach is the order of three while existing are fourth order. Empirical experiment conducted on standard benchmark datasets established the relevance of the proposed approach