Background: Headaches have not only medical but also great socioeconomic significance, therefore, it is necessary to evaluate the overall impact of headaches on a patient’s life, including their work and work efficiency. The aim of this study was to determine the impact of individual headache types on work and work efficiency. Methods: This research was designed as a cross-sectional study performed by administering a questionnaire among employees. The questionnaire consisted of general questions, questions about headache features, and questions about the impact of headaches on work. Results: Monthly absence from work was mostly represented by migraine sufferers (7.1%), significantly more than with sufferers with tension-type headaches (2.23%; p = 0.019) and other headache types (2.15%; p = 0.025). Migraine sufferers (30.2%) worked in spite of a headache for more than 25 h, which was more frequent than with sufferers from tension-type and other-type headaches (13.4%). On average, headache sufferers reported work efficiency ranging from 66% to 90%. With regard to individual headache types, this range was significantly more frequent in subjects with tension-type headaches, whereas 91–100% efficiency was significantly more frequent in subjects with other headache types. Lower efficiency, i.e., 0–40% and 41–65%, was significantly more frequent with migraine sufferers. Conclusions: Headaches, especially migraines, significantly affect the work and work efficiency of headache sufferers by reducing their productivity. Loss is greater due to reduced efficiency than due to absenteeism.

Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It has been studied in various research fields: data mining, machine learning, pattern recognition and in engineering, economics and biomedical data analysis. Headache is not a disease that typically shortens one’s life, but it can be a serious social as well as a health problem. Approximately 27 billion euros per year are lost through reduced work productivity in the European community. This paper is focused on a new strategy based on a hybrid model for combining fuzzy partition method and maximum likelihood estimation clustering algorithm for diagnosing primary headache disorder. The proposed hybrid system is tested on two data sets for diagnosing headache disorder collected from Clinical Centre of Vojvodina in Serbia.

Inventory management deals with a tradeoff between the benefits of keeping stocks of goods that allows fulfillment of the customer’s demand, and the cost of carrying inventory. Inventory control techniques are very important components and the most organizations can substantially reduce their costs associated with the flow of materials. This paper presents new inventory management model based on particle swarm optimization and pure adaptive search global optimization algorithm in production-inventory system. The proposed model is focusing on planned level of demand for finished goods, production and raw materials cost, production capacity as the norm, change of the production cost and inventory capital cost, all of which are typical factors in automobile manufacture industry. The model determines different factors such as the minimizing inventory quantity, minimizing inventory value, and minimizing production cost based on demand for production items. The model is tested with original real-world dataset obtained from the automotive company Lear from US and its factory in Novi Sad, Serbia.

(1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelligence techniques, among which the most important are: Calinski-Harabasz index, Analytical Hierarchy Process, and Weighted Fuzzy C-means Clustering Algorithm. These methods, techniques and methodologies are used to create a hybrid intelligent system for diagnosing primary headache disorders. The proposed intelligent diagnostic system is tested with original real-world data set with different metrics. (3) Results: First at all, nine of 20 attributes – features from International Headache Society (IHS) criteria are selected, and then only five most important attributes from IHS criteria are selected. The calculation result based on the Calinski–Harabasz index value (178) for the optimal number of clusters is three, and they present three classes of headaches: (i) migraine, (ii) tension-type headaches (TTHs), and (iii) other primary headaches (OPHs). The proposed hybrid intelligent system shows the following quality metrics: Accuracy 75%; Precision 67% for migraine, 74% for TTHs, 86% for OPHs, and Average Precision 77%; Recall 86% for migraine, 73% for TTHs, 67% for OPHs, Average Recall 75%; F₁ score 75% for migraine, 74% for TTHs, 75% for OPHs, and Average F₁ score 75%. (4) Conclusions: The hybrid intelligent system presents qualitative and respectable experimental results. The implementation of existing diagnostics systems and the development of new diagnostics systems in medicine is necessary in order to help physicians make quality diagnosis and decide the best treatments for the patients.