Utilizing machine learning to identify fall predictors in India's aging population: findings from the LASI
An association between falls and depression was observed. Depressive symptoms were associated with an increased risk of falls, even after controlling for other co-factors. The CIT method leveraged us to select the most important variables to predict falls with great precision. To prevent and manage falls among the expanding and diverse older-aged population, a multilevel and cross-sectoral approach is required. Mental health, especially depression, should be dealt with greater precautions. Public health enthusiasts should focus on the physical as well as mental health of the country's older adult population.
Authors
- Mrinmoy Pratim Bharadwaz (ORCID: https://orcid.org/0000-0001-9124-4323)
- Jumi Kalita (ORCID: https://orcid.org/0000-0003-4936-0042)
- Aditi Aditi (ORCID: https://orcid.org/0000-0003-2750-5685)
- Anandita Mitro (ORCID: https://orcid.org/0009-0005-3217-5336)
Institutions
- International Institute for Population Sciences (IN)
- Birla Institute of Technology and Science - Hyderabad Campus (IN)
Publication Details
- Journal
- BMC Geriatrics
- Published
- 2025-03-17
- DOI
- https://doi.org/10.1186/s12877-025-05813-z
- Citations
- 1
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 4.76