| Popis: |
Prevention and diagnosis of frailty syndrome (FS) in cardiac patients require innovative systems supporting medical personnel and patient adherence and self-care behavior. Modern medicine uses artificial intelligence (AI) 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 individual components of the Tilburg frailty Indicator (TFI) syndrome questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and permutation method to determine the absolute importance of frailty components in HF. Based on the TFI data, which contains 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 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: difficulty walking, lack of strength in the hands 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 obtained results do not allow for the rejection of the null hypothesis. Our study justified the AI based approach for developing and improving existing frailty measurements in patients with HF. In long-term perspective, the AI based frailty approach can support healthcare professionals, including psychologists and social workers in drawing their attention to non-physical origins of HF. |
