Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm

• Published in: Diagnostics (Basel) Vol. 12; no. 11; p. 2844
• Main Authors: Hasanin, Tawfiq, Kshirsagar, Pravin, Manoharan, Hariprasath, Sengar, Sandeep, Selvarajan, Shitharth, Satapathy, Suresh
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.11.2022; MDPI
• Subjects: Accuracy; Algorithms; audio features; Automation; Deep learning; depression prediction; Design; Despair; Evaluation; Machine learning; Medical research; Mental depression; Mental disorders; Mental health care; mental imbalance; Patients; Psychological tests; Research methodology; Speech; Support vector machines; Young adults; Saudi Arabia; deep learning; depression prediction; mental imbalance; audio features
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics12112844

The majority of people in the modern biosphere struggle with depression as a result of the coronavirus pandemic’s impact, which has adversely impacted mental health without warning. Even though the majority of individuals are still protected, it is crucial to check for post-corona virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, and if they are not recognised at an early stage, the risks will be increased. Additionally, if the post-symptoms are severe and go untreated, it might harm one’s mental health. In order to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person’s mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%.