The week's pick
Reseach Article
Machine Learning based Detection of Depression and Anxiety
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 45 |
| Year of Publication: 2021 |
| Authors: Guna Sekhar Sajja |
10.5120/ijca2021921856
|
Guna Sekhar Sajja . Machine Learning based Detection of Depression and Anxiety. International Journal of Computer Applications. 183, 45 ( Dec 2021), 20-23. DOI=10.5120/ijca2021921856
Abstract
Anxiety is something that everyone encounters at some point in their lives. Anxiety is a word that is used in everyday situations to represent the discomforts and negative feelings that a person has when they are tense or worried. Machine learning approaches enable computers to create data that may be utilized for factual study to attain a certain range of performances. The use of computer frameworks to automate decision-making based on test data is encouraged throughout the development of the models for the test data. This article presents a model for predicting feelings of anxiety and depression. There is a set of speech data that is used as input into this framework. A preprocessing step was performed on this data set to remove noise from the data and make the original data set more consistent. The input data set is then submitted to a variety of machine learning approaches, including Nave Bayes, Random Forest, and Support Vector Machines (SVM). It is necessary to classify the information. In this section, the categorization results of several approaches are discussed and contrasted.
References
- A. Ahmed, R. Sultana, M. T. R. Ullas, M. Begom, M. M. I. Rahi and M. A. Alam, "A Machine Learning Approach to detect Depression and Anxiety using Supervised Learning," 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2020, pp. 1-6, doi: 10.1109/CSDE50874.2020.9411642.
- Priya, A., Garg, S., &Tigga, N. (2020). Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms. Procedia Computer Science, 167, 1258-1267. doi: 10.1016/j.procs.2020.03.442
- Richter, T., Fishbain, B., Markus, A. et al. Using machine learning-based analysis for behavioral differentiation between anxiety and depression. Sci Rep 10, 16381 (2020). https://doi.org/10.1038/s41598-020-72289-9
- Su, C., Xu, Z., Pathak, J. et al. Deep learning in mental health outcome research: a scoping review. Transl Psychiatry 10, 116 (2020). https://doi.org/10.1038/s41398-020-0780-3
- Sharma A and Verbeke WJMI (2020) Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n = 11,081). Front. Big Data 3:15. doi: 10.3389/fdata.2020.00015
- Cacheda F, Fernandez D, Novoa FJ, Carneiro V Early Detection of Depression: Social Network Analysis and Random Forest Techniques J Med Internet Res 2019;21(6):e12554 doi: 10.2196/12554
- Nemesure, M.D., Heinz, M.V., Huang, R. et al. Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence. Sci Rep 11, 1980 (2021). https://doi.org/10.1038/s41598-021-81368-4
- Kumar, P., Garg, S., & Garg, A. (2020). Assessment of Anxiety, Depression and Stress using Machine Learning Models. Procedia Computer Science, 171, 1989-1998. doi: 10.1016/j.procs.2020.04.213
- Ihmig FR, H. AG, Neurohr-Parakenings F, Schäfer SK, Lass-Hennemann J, Michael T (2020) On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals. PLoS ONE 15(6): e0231517. https://doi.org/10.1371/journal.pone.0231517
- Moshe I, Terhorst Y, Opoku Asare K, Sander LB, Ferreira D, Baumeister H, Mohr DC and Pulkki-Råback L (2021) Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data. Front. Psychiatry 12:625247. doi: 10.3389/fpsyt.2021.625247
- M. Huang, "Theory and Implementation of linear regression," 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), 2020, pp. 210-217, doi: 10.1109/CVIDL51233.2020.00-99.
- K. Taunk, S. De, S. Verma and A. Swetapadma, "A Brief Review of Nearest Neighbor Algorithm for Learning and Classification," 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 2019, pp. 1255-1260, doi: 10.1109/ICCS45141.2019.9065747.
- A. Rajeshkanna and K. Arunesh, "ID3 Decision Tree Classification: An Algorithmic Perspective based on Error rate," 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 2020, pp. 787-790, doi: 10.1109/ICESC48915.2020.9155578.
- Y. Yadav, V. Kumar, V. Ranga and R. M. Rawat, "Analysis of Facial Sentiments: A deep-learning Way," 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 2020, pp. 541-545, doi: 10.1109/ICESC48915.2020.9155622.
- J. Huang, J. Zhou and L. Zheng, "Support Vector Machine Classification Algorithm Based on Relief-F Feature Weighting," 2020 International Conference on Computer Engineering and Application (ICCEA), 2020, pp. 547-553, doi: 10.1109/ICCEA50009.2020.00121.
- Z. Bingzhen, Q. Xiaoming, Y. Hemeng and Z. Zhubo, "A Random Forest Classification Model for Transmission Line Image Processing," 2020 15th International Conference on Computer Science & Education (ICCSE), 2020, pp. 613-617, doi: 10.1109/ICCSE49874.2020.920190.
Index Terms
Keywords