Depression Detection with Convolutional Neural Networks: A Step Towards Improved Mental Health Care

Detection and prediction of Future Mental disorder from Social Media Data using Machine Learning, Ensemble Learning, and Large Language Models.

“…Kumar et al [8] 2019 100 users 1 (depression) 3 ML models Acc= 85% Tarik et al [9] 2019 Not mentioned 1 (depression) 2 DL models Acc= 74% Hussain et al [10] 2019 Not mentioned 2 (depression & anxiety) 3 ML models F1-score= 0.84 Wong et al [11] 2019 Not mentioned 1 (depression) 2 DL models -Rezaii et al [12] 2019 40 users 1 (depression) 2 NLP techniques Acc= 90% Inkpen et al [13] 2019 Not mentioned 2 (depression and PTSD) 1 DL model Acc= 88% Thorstad et al [6] 2019 All REDDIT 4 (all mental disorders) 1 ML model F1-score= 0.77 Trifan et al [14] 2020 Not mentioned 1 (depression) 3 ML models F1-score= 0.72 Jiang et al [15] 2020 Not mentioned 4 (all mental disorders) 1 DL model F1-score= 0.64 Alghamdi et al [16] 2020 Not mentioned 1 (depression) 6 ML models Acc= 80% Birnbaum et al [17] 2020 223 users 1 (depression) 2 ML models Acc= 77% Chatterjee et al [18] 2021 Not mentioned 1 (depression) 1 ML models Acc= 76% Ren et al [19] 2021 Not mentioned 1 (depression) 1 DL models Acc= 91% Shaoxiong et al [20] 2022 All REDDIT 1 (depression) 2 EL models Acc= 75% Nalini. L [21] 2022 Not mentioned Not mentioned 3 ML models Not mentioned Tufail [22] 2023 Not mentioned 1 (depression) 1 DL model Acc= 64% Koushik et al [23] 2023 Not mentioned 1 (depression) 1 ML & 2 DL Acc= 60% Yicheng et al [25] 2023 Not mentioned 1 (depression) 1 Time series approach -Helmy et al [27] 2024 70,000 tweets 1 (depression) 5 ML models Acc=92% Dhariwal [28] 2024 Small healthcare dataset The proposed work aims at the early detection and even the prediction of potential future mental disorder from social media data. This successful approach can be used for effectively diagnosing mental disorders of social media users without asking them to cooperate in the diagnosis process.…”

“…In 2023 Tufail [22] proposed a depression detection approach using convolution neural networks (CNNs), and they confirmed that they achieved a validation accuracy rate of only 64%. The authors then confirmed that they were able to increase the accuracy from 64% to 68% when they used complex data generation and augmentation methods, but this still very low rate of accuracy compared to other work done in literature.…”