Abstract:
Depression is a significant healthcare problem worldwide, impacting countless people of all ages annually. This paper presents a novel approach for depression identification by using complex datasets and employing various machine learning models such as LightGBM, XGBoost, Naïve Bayes, and Random Forest. The study emphasizes how itis possible to change the face of mental health diagnosis with those models relying on the data. In the future, the analysis should aim at improving model interpretability, reducing bias in the algorithms, using multi-source data as well ethical issues such as rights of privacy and consent. These innovations are designed to help improve the diagnostic capabilities, promote inclusiveness in the outcomes, and support the means of real-time monitoring their health of the individual. Among the models tested, LightGBM proved to be the best as it achieved an accuracy of 83.67% and an F1 score of 84.06%. XGBoost had a higher accuracy rate while Naive Bayes had a higher recall rate. Random Forest also proved effective, performing well in all areas metrics which show their different benefits for dealing with issues in mental health care.
