upon the earlier identification of the intricate
relationship between psychological states and
cardiovascular health, this study expanded traditional
datasets to include mental health indicators, creating
a more holistic model of the complex interplay
between mind and body. The Logistic Regression
classifier yielded the most promising results, which
achieved high accuracy and skillfully balanced
precision and recall, vital for clinical applicability.
The insights gained reinforce the necessity for
classifiers that can navigate the delicate intricacies of
medical diagnostics. In the future, this study intends
to refine the selection of features further, delve deeper
into the models' interpretability, and broaden the
scope to encapsulate an enormous array of health
predictors, continuing the commitment to advance
predictive analytics in public health.
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