## 1 Introduction  
Neurological control through muscle and sensing is part of almost every human activity. It is so natural that we do not even realize it. This is also the case for speech production. Even though the process itself is complex, its functioning is taken for granted. Unfortunately, neurological diseases, infections, tissue changes, and injuries can negatively affect speech production. Impaired speech production is frequently represented by dysphonia or dysphonic voice. Dysphonia is a perceptual quality of the voice that indicates that some negative changes have occurred in the phonation organs [1]. The relationship between voice pathology and acoustic voice features has been clinically established and confirmed both quantitatively and subjectively by speech experts [2; 3; 4].  
As indicated above, pathological voice is an indication of health-related problems. However, recognizing dysphonic voice at an early stage is not an easy task. Usually, a trained expert is required, and a series of speech exercises need to be completed. Automatized speech evaluation can allow for time- and cost-effective speech analysis that, in turn, enables the screening of a wider population. Since some diseases, such as Parkinson's disease, manifest themselves early in speech disruption, early discovery through screening can lead to earlier treatment and to improved treatment results. The main motivation for the realization of this work is the utilization of artificial intelligence for the diagnosis of different diseases. This can lead to significant improvements in diagnosis and healthcare and, in turn, to the improvement of human life [5; 6]. Several diseases include a speech disorder as an early symptom. This is usually due to damage of the neurological system or caused directly by damage to some part of the vocal tract, such as the vocal cords [7]. Frequently, a speech disorder leads to a secondary symptom, and early detection may reveal many high-risk illnesses [1; 8].  
The ultimate goal of this research is to develop a decision support system that provides an accurate, objective, and time-efficient diagnosis and helps medical personnel provide the correct diagnostic decision and treatment. In this work, we focused on the detection of pathological speech based on features obtained from voice samples from multiple subjects. It is a noninvasive way of examining a voice and only digital voice recordings are needed. Voice data were obtained from the publicly available Saarbrucken Voice Database (SVD). We exported 194 samples from SVD, of which 94 samples originated from patients with dysphonia and 100 samples came from healthy ones. In order to detect pathological speech, it is necessary to build and train a classification model. For this purpose, three state-of-the-art classification algorithms were utilized: support vector machine (SVM), random forest classifier (RFC), and K-nearest neighbors (KNN).  
The paper is organized as follows. In the next section, we provide a brief overview of similar works in the area of pathological speech processing. Then, we describe the preprocessing of the dataset and provide a brief overview of classification algorithms. Later, we propose a decision support model and present the results of numerical experiments. Conclusions are drawn in the last section.