Article: An improved machine learning based system for depression detection with RFLR model Journal: International Journal of Bioinformatics Research and Applications (IJBRA) 2025 Vol.21 No.6 pp.599 - 622 Abstract: The world is changing at a dizzying pace due to technological advancements and improved human abilities. Physical and emotional well-beings suffer due to the immense strain of keeping up with the fast-paced society around us. Depression is a prevalent mental condition that affects everyone at some time. Globally, millions of individuals suffer from depression, making it one of the most prevalent mental illnesses. Prolonged and excessive fretting about several issues that a healthy person would often dismiss as unimportant characterises depression. Machine learning algorithms are crucial for deciphering healthcare data and revealing hidden information. In the investigated approach, a hybrid model was utilised to combine RF and LR using a voting classifier to construct a depression prediction model. After acquiring a suitable dataset from Kaggle, the suggested method for depression prediction moves on to pre-processing, where data is cleaned and scaled to guarantee consistency and quality. The proposed model is trained and tested with the 'b_depressed.csv' dataset obtained from a Kaggle source with 1,767 records. The model demonstrates superior performance in both accuracy and overall effectiveness compared to other models, as indicated by the findings. Inderscience Publishers - linking academia, business and industry through research

Title: An improved machine learning based system for depression detection with RFLR model

Authors: S. Nalini Poornima; S. Geetha

Addresses: Department of Computer Science and Engineering, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, Dr. M.G.R. Educational and Research Institute, Maduravoyal, Chennai, Tamil Nadu, India

Abstract: The world is changing at a dizzying pace due to technological advancements and improved human abilities. Physical and emotional well-beings suffer due to the immense strain of keeping up with the fast-paced society around us. Depression is a prevalent mental condition that affects everyone at some time. Globally, millions of individuals suffer from depression, making it one of the most prevalent mental illnesses. Prolonged and excessive fretting about several issues that a healthy person would often dismiss as unimportant characterises depression. Machine learning algorithms are crucial for deciphering healthcare data and revealing hidden information. In the investigated approach, a hybrid model was utilised to combine RF and LR using a voting classifier to construct a depression prediction model. After acquiring a suitable dataset from Kaggle, the suggested method for depression prediction moves on to pre-processing, where data is cleaned and scaled to guarantee consistency and quality. The proposed model is trained and tested with the 'b_depressed.csv' dataset obtained from a Kaggle source with 1,767 records. The model demonstrates superior performance in both accuracy and overall effectiveness compared to other models, as indicated by the findings.

Keywords: depression prediction; classification; pre-processing; random forest; RF; logistic regression; LR; machine learning based system; depression detection; RFLR model.

DOI: 10.1504/IJBRA.2025.150101

International Journal of Bioinformatics Research and Applications, 2025 Vol.21 No.6, pp.599 - 622

Received: 12 May 2024
Accepted: 05 Aug 2024
Published online: 01 Dec 2025 *

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Title: An improved machine learning based system for depression detection with RFLR model

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