The prevention and diagnosis of frailty syndrome (FS) in cardiac patients requires innovative systems to support medical personnel, patient adherence, and self-care behavior. To do so, modern medicine uses a supervised machine learning approach (ML) to study the psychosocial domains of frailty in cardiac patients with heart failure (HF). This study aimed to determine the absolute and relative diagnostic importance of the individual components of the Tilburg Frailty Indicator (TFI) questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and the permutation method to determine the absolute importance of frailty components in HF. Based on the TFI data, which contain physical and psychosocial components, machine learning models were built based on three algorithms: a decision tree, a random decision forest, and the AdaBoost Models classifier. The absolute weights were used to make pairwise comparisons between the variables and obtain relative diagnostic importance. The analysis of HF patients’ responses showed that the psychological variable TFI20 diagnosing low mood was more diagnostically important than the variables from the physical domain: lack of strength in the hands and physical fatigue. The psychological variable TFI21 linked with agitation and irritability was diagnostically more important than all three physical variables considered: walking difficulties, lack of hand strength, and physical fatigue. In the case of the two remaining variables from the psychological domain (TFI19, TFI22), and for all variables from the social domain, the results do not allow for the rejection of the null hypothesis. From a long-term perspective, the ML based frailty approach can support healthcare professionals, including psychologists and social workers, in drawing their attention to the nonphysical origins of HF.
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Tipping is a social norm in many countries and has important functions as a source of income, with significant social welfare effects. Tipping can also represent a form of lost tax revenue, as service workers and restaurants may not declare all cash tips. These interrelationships remain generally insufficiently understood. This paper presents the results of a comparative survey of resident tipping patterns in restaurants in Spain, France, Germany, Switzerland, Sweden, Norway, and the Netherlands. ANOVA and ANCOVA analyses confirm significant variation in tipping norms between countries, for instance with regard to the frequency of tipping and the proportion of tips in relation to bill size. The paper discusses these findings in the context of employment conditions and social welfare effects, comparing the European Union minimum wage model to gratuity-depending income approaches in the USA. Results have importance for the hospitality sector and policymakers concerned with social welfare
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