Parkinson's disease is a type of disease caused by the loss of dopamine-producing cells in the brain. As the amount of dopamine decreases, the symptoms of Parkinson's disease emerge. Parkinson's disease is a slow-developing disease, and symptoms such as hands, arms, legs, chin and face tremors are increasing over time. As the disease progresses, people may have difficulty in walking and speaking. There is no definitive treatment for Parkinson's disease; however, with the help of some drugs, the symptoms of the disease can be reduced. Although there is no definitive treatment for Parkinson's disease, the patient can continue his normal life by controlling the problems caused by the disease. At this point, it is important to prevent early detection and progression of the disease. In this study, different types of classification methods such as Logistic regression, Support Vector Machine, Extra Trees, Gradient Boosting and Random Forest are compared in order to predict Parkinson's disease. A total of 1208 speech data sets consisting of 26 features obtained from Parkinson's patients and non-patients were used in the classification stage. The feature space of the dataset is expanded due to correlation maps. These correlation maps are constructed with the features which are obtained by using Principal Component Analysis (PCA), Information Gain (IG) and all features respectively. It is concluded that, classification results which are attained with expanded features outperform the classification results attained with the original features of the data.

Latent Dirichlet allocation (LDA) is one of the probabilistic topic models; it discovers the latent topic structure in a document collection. The basic assumption under LDA is that documents are viewed as a probabilistic mixture of latent topics; a topic has a probability distribution over words and each document is modelled on the basis of a bag-of-words model. The topic models such as LDA are sufficient in learning hidden topics but they do not take into account the deeper semantic knowledge of a document. In this article, we propose a novel method based on topic modelling to determine the latent aspects of online review documents. In the proposed model, which is called Concept-LDA, the feature space of reviews is enriched with the concepts and named entities, which are extracted from Babelfy to obtain topics that contain not only co-occurred words but also semantically related words. The performance in terms of topic coherence and topic quality is reported over 10 publicly available datasets, and it is demonstrated that Concept-LDA achieves better topic representations than an LDA model alone, as measured by topic coherence and F-measure. The learned topic representation by Concept-LDA leads to accurate and an easy aspect extraction task in an aspect-based sentiment analysis system.

In the world we live in, people from different professions are at increased risk for depressive symptoms and posttraumatic stress disorder (PTSD) due to hard working or extreme environmental conditions. Accurate diagnosis and determining the causes are very important to solve these kinds of psychological problems. Machine learning (ML) techniques are gaining popularity in neuroscience due to their high diagnostic capability and effective classification ability. In this paper, alternative hybrid systems which allowed us to develop automatic classifiers for finding the Posttraumatic stress disorder (PTSD) patients are proposed and compared. With the proposed system, not only the PTSD individuals are classified by ML techniques such as sequential minimal optimization (SMO), multilayer perceptron (MLP), Naïve Bayes (NB) but also the important indications of patients' trauma are determined by three popular feature selection methods such as chi-square, principal component analysis (PCA) and correlation based-feature selection (CFS). The effectiveness of the proposed system is examined on a real world dataset. Due to obtained results we can estimate the individuals as PTSD or NONPTSD patients with 74-79% accuracy range, further to that instead of 39 features 7 features are remarked as the most critical symptoms for PTSD.