Clustering is a powerful machine learning tool for detecting structures in datasets. In the medical field, clustering has been proven to be a powerful tool for discovering patterns and structure in labeled and unlabeled datasets. Unlike supervised methods, clustering is an unsupervised method that works on datasets in which there is no outcome (target) variable nor is anything known about the relationship between the observations, that is, unlabeled data. In this paper, we focus on studying and reviewing clustering methods that have been applied to datasets of neurological diseases, especially Alzheimer’s disease (AD). The aim is to provide insights into which clustering technique is more suitable for partitioning patients of AD based on their similarity. This is important as clustering algorithms can find patterns across patients that are difficult for medical practitioners to find. We further discuss the implications of the use of clustering algorithms in the treatment of AD. We found that clustering analysis can point to several features that underlie the conversion from early-stage AD to advanced AD. Furthermore, future work can apply semi-clustering algorithms on AD datasets, which will enhance clusters by including additional information.

We apply our new fuzzy Monte Carlo method to a certain fuzzy linear regression problem to estimate the best solution. The best solution is a vector of triangular fuzzy numbers, for the fuzzy coefficients in the model, which minimizes one of two error measures. We use a quasi-random number generator to produce random sequences of these fuzzy vectors which uniformly fill the search space. We consider an example problem and show this Monte Carlo method obtains the best solution for one error measure and is approximately best for the other error measure.

a b s t r a c tMeasuring toxicity is an important step in drug development. However, the current experimental methods which are used to estimate the drug toxicity are expensive and need high computational efforts. Therefore, these methods are not suitable for large-scale evaluation of drug toxicity. As a consequence, there is a high demand to implement computational models that can predict drug toxicity risks. In this paper, we used a dataset that consists of 553 drugs that biotransformed in the liver. In this data, there are four toxic effects, namely, mutagenic, tumorigenic, irritant and reproductive effects. Each drug is represented by 31 chemical descriptors. This paper proposes two models for predicting drug toxicity risks. The proposed models consist of three phases. In the first phase, the most discriminative features are selected using rough set-based methods to reduce the classification time and improve the classification performance. In the second phase, three different sam pling algorithms, namely, Random Under-Sampling, Random Over-Sampling, and Synthetic Minority Oversampling Technique (SMOTE) are used to obtain balanced data. In the third phase, the first proposed model employs the Neutrosophic Rule-based Classification System (NRCS), and the second model uses Genetic NRCS (GNRCS) to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed models obtained high sensitivity (89-93%), specificity (91-97%), and GM (90-94%) for all toxic effects. Overall, the results of the proposed models indicate that it could be utilized for the prediction of drug toxicity in the early stages of drug development.

We apply our new fuzzy Monte Carlo method to a certain fuzzy linear regression problem to estimate the best solution. The best solution is a vector of crisp numbers, for the coefficients in the model, which minimizes one of two error measures. We use a quasi-random number generator to produce random sequences of these crisp vectors which uniformly fill the search space. We consider an example problem and show this Monte Carlo method obtains the best solution for both error measures.

The highly spreading virus, COVID-19, created a huge need for an accurate and speedy diagnosis method. The famous RT-PCR test is costly and not available for many suspected cases. This article proposes a neurotrophic model to diagnose COVID-19 patients based on their chest X-ray images. The proposed model has five main phases. First, the speeded up robust features (SURF) method is applied to each X-ray image to extract robust invariant features. Second, three sampling algorithms are applied to treat imbalanced dataset. Third, the neutrosophic rule-based classification system is proposed to generate a set of rules based on the three neutrosophic values < T; I; F>, the degrees of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to select the optimal neutrosophic rules to improve the classification performance. Fifth, in this phase, the classification-based neutrosophic logic is proposed. The testing rule matrix is constructed with no class label, and the goal of this phase is to determine the class label for each testing rule using intersection percentage between testing and training rules. The proposed model is referred to as GNRCS. It is compared with six state-of-the-art classifiers such as multilayer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest classifiers (RFC) with quality measures of accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed model is powerful for COVID-19 recognition with high specificity and high sensitivity and less computational complexity. Therefore, the proposed GNRCS model could be used for real-time automatic early recognition of COVID-19.