The study and comparative analysis of machine learning algorithms for evaluating their effectiveness in dementia classification task
Journal Title: Modern Innovations, Systems and Technologies - Year 2025, Vol 5, Issue 1
Abstract
This work presents a study and comparative analysis of machine learning algorithms for the classification of dementia in elderly patients. The relevance of the topic is due to the significant increase in dementia cases and the importance of timely diagnosis for improving the quality of life. The main goal of the research is to determine the effectiveness of various classification methods applied to a wide range of medical data about patients aged 60 to 90, including demographic and clinical characteristics as well as cognitive test results. The work covers data preprocessing stages, the application of various machine learning algorithms, and their subsequent analysis. The methods discussed include decision trees, random forests, the K-nearest neighbors method, logistic regression, and gradient boosting. Additionally, the study emphasizes the significance of model interpretability and potential limitations related to data sampling. The results indicate that machine learning methods can significantly enhance dementia diagnosis, opening new prospects for early intervention and resource optimization in the healthcare system. The work provides useful recommendations for further research in this area, while also highlighting the importance of integrating new technologies into practical medicine.
Authors and Affiliations
A. P. Pashkovskaya
Keywords
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- EP ID EP770131
- DOI 10.47813/2782-2818-2025-5-1-1041-1047
- Views 4
- Downloads 0
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