TY - GEN
T1 - Hybrid Machine Learning Model for Detecting Depression on its Early Stage
AU - Kokila, S.
AU - Yashaswini, K. A.
AU - Manasa, C. M.
AU - Preethi, null
AU - Sangani, Sangeetha Parameshwar
AU - Sapna, R.
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The proposed study presents a simplified and yet innovative idea of addressing the early detection problem for depression from social media post. Review of literature suggest different variants of machine learning model towards detection of depression; however, there are open-end issues. Hence, the current work introduced a hybridized machine learning model using Gradient Boosting, Random Forest, and Support Vector Machine. The model also contributes to a novel manifold feature extraction approach to enrich the final feature vector quality. The prime contribution of this study is its improved generalization, adaptability, and scalability. Tested with massive benchmarked dataset of social media, the proposed model exhibited 97% accuracy and 25% faster response time in contrast to standalone version of existing machine learning model.
AB - The proposed study presents a simplified and yet innovative idea of addressing the early detection problem for depression from social media post. Review of literature suggest different variants of machine learning model towards detection of depression; however, there are open-end issues. Hence, the current work introduced a hybridized machine learning model using Gradient Boosting, Random Forest, and Support Vector Machine. The model also contributes to a novel manifold feature extraction approach to enrich the final feature vector quality. The prime contribution of this study is its improved generalization, adaptability, and scalability. Tested with massive benchmarked dataset of social media, the proposed model exhibited 97% accuracy and 25% faster response time in contrast to standalone version of existing machine learning model.
UR - https://www.scopus.com/pages/publications/105014482878
UR - https://www.scopus.com/pages/publications/105014482878#tab=citedBy
U2 - 10.1109/I2CACIS65476.2025.11100499
DO - 10.1109/I2CACIS65476.2025.11100499
M3 - Conference contribution
AN - SCOPUS:105014482878
T3 - 2025 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2025 - Proceedings
SP - 232
EP - 237
BT - 2025 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2025 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2025
Y2 - 27 June 2025 through 28 June 2025
ER -