Identifying patterns of depression in the elderly through data mining
DOI:
https://doi.org/10.59681/2175-4411.v16.2024.1020Keywords:
Depression, Data Mining, Machine LearningAbstract
Objective: Identify patterns of depression in elderly people based on exogenous variables through data mining. Methods: The process applies the Random Forest classification technique to describe the patterns of depression in this population. The PNS, IBGE 2013 database is considered as a data source. Results: The results highlight pre-existing chronic diseases, level of trust with friends and relatives, level of education, etc. as relevant factors. For the group diagnosed “With depression”, the accuracy of the model was 68.8%, sensitivity of 77.2% and F1-score measurement of 72.8%. For the group diagnosed “No depression”, the accuracy was 66.4%, Sensitivity was 56.2% and the F1-score measure was 60.9%. Conclusion: Among the factors that stand out, in terms of importance, are pre-existing chronic illness, one or no relatives or friends to trust, and education up to high school. Practicing physical exercise and staying active is a favorable aspect for non-depression.
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