Integrated Learning-Based Algorithm for Predicting Graduates’ Employment Mental Health

• Published in: Mathematical problems in engineering Vol. 2022; pp. 1 - 9
• Main Authors: Dongrui, Chen, Shengjie, Wang, Kate, Wen
• Format: Journal Article
• Language: English
• Published: New York Hindawi 25.06.2022; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Classification; Classifiers; College students; Colleges & universities; Data analysis; Data mining; Decision analysis; Decision trees; Education; Employment; Entrepreneurs; Feature extraction; Learning; Machine learning; Mathematical problems; Mental disorders; Mental health; Neural networks; Pattern recognition; Pessimism; Predictions; Society; Stress; Students; Study abroad; University graduates
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2022/5761815

Adaboost is a mental health prediction method that utilizes an integrated learning algorithm to address the current state of mental health issues among graduates in the workforce. The method first extracts the features of mental health test data, and after data cleaning and normalization, the data are mined and analyzed using a decision tree classifier. The Adaboost algorithm is then used to train the decision tree classifier for multiple iterations in order to improve its classification efficiency, and a mental health prognosis model is constructed. Using the model, 2780 students in the class of 2022 at a university were analyzed. The trial results demonstrated that the strategy was capable of identifying sensitive psychological disorders in a timely manner, providing a basis for making decisions and developing plans for mental health graduate students.