Data Mining methods and machine learning as tools for analyzing patient conditions and disease complications based on clinical data

Abstract

The article investigates the possibilities of applying Data Mining methods and machine learning techniques for the analysis of clinical data in the context of complex medical conditions characterized by high heterogeneity of disease progression and an increased risk of complications. The relevance of the study is determined by the rapid digitalization of the healthcare system, the growth of medical data volumes, and the need to implement personalized approaches to diagnosis, treatment, and rehabilitation of patients. Particular attention is paid to data analysis in pediatric practice and endocrinology, where traditional statistical methods are often insufficient for identifying complex nonlinear relationships. The objects of the study include two clinical cohorts: children aged from six months to sixteen years with respiratory system diseases associated with post-traumatic stress disorder, and adult patients with type 1 diabetes mellitus presenting various complications of disease progression. Within the scope of the research, a comprehensive approach was applied that combines descriptive statistical methods with machine learning algorithms, including ensemble models, feature importance evaluation methods, and clustering analysis. For the pediatric cohort, relationships between bronchitis severity, frequency of acute respiratory viral infections, post-traumatic stress indicators, sleep disorders, an integrated quality-of-life index, and the intensity of hostilities in the region of residence were analyzed. For the cohort of patients with type 1 diabetes mellitus, complications of disease progression were analyzed using classification and clustering methods, which made it possible to identify internal risk stratification even within clinically binary outcome variables. The results demonstrate that the application of machine learning methods not only improves the accuracy of clinical data analysis but also enables the identification of latent patient groups with different degrees of disease severity and potential risk of complication development. The proposed approach can be used as a clinical decision support tool and as a basis for further research in the field of personalized and preventive medicine

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