Angelia Melani Adrian, Junaidy Budi Sanger
Worldwide, 264 million people suffer from depression, which is one of the main causes of inefficiency. Poor working environments can cause a variety of physical and mental problems. According to studies, people are afraid to seek help because of the societal stigma associated with mental health issues. Considering the height of machine learning, we can use various machine learning algorithms to predict depression in humans. Depression is a frequent and significant medical condition that adversely affects our thoughts, feelings, and actions. It might be fatal since a person in this mindset has given up hope and only perceives the negative sides of the circumstance. According to a 2019 WHO report, approximately 3 million people globally between the ages of 17–25 and 40–70 suffer from depression. Thus in such cases, machine learning models can be developed on a training database to determine whether or not a person is depressed. Users can interact with their loved ones and post their ideas, photos, and videos that reflect their sentiments, moods, and emotions on social media, which is a fantastic source of realistic data from online users. This opens up the possibility of analyzing social networking data to detect users’ emotions and moods in order to investigate their feelings and behavior when interacting on these online platforms. Many researchers have attempted to predict depression using machine learning approaches in the past. The purpose of this paper is to review the effectiveness of ML strategies for the early prediction of depression in humans. © 2024 Scrivener Publishing LLC.
Informatics Engineering Department, Universitas Katolik De La Salle, Manado, Indonesia
