Machine Learning Models to Classify and Predict Depression in College Students

Authors

  • Orlando Iparraguirre-Villanueva Universidad Tecnológica del Perú https://orcid.org/0000-0001-8185-2034
  • Cleoge Paulino-Moreno Universidad Católica de Trujillo, Trujillo, Perú
  • Andrés Epifanía-Huerta Universidad Tecnológica del Perú
  • Carmen Torres-Ceclén Universidad Católica los Ángeles de Chimbote

DOI:

https://doi.org/10.3991/ijim.v18i14.48669

Keywords:

classification, prediction, depression, machine learning, students

Abstract


Depression is an increasingly common mental health condition worldwide and is influenced by various factors such as anxiety, frustration, obesity, medical issues, etc. In severe cases, it can even result in suicide. This study aimed to utilize machine learning (ML) models to categorize and forecast student depression. The research involved analyzing a dataset of 787 college students through a series of steps, including cleansing, model training, and testing using techniques to classify and predict student depression. Three ML models were employed: logistic regression (LR), K-nearest neighbor (KNN), and decision tree (DT). The findings revealed that the LR model achieved the highest accuracy in prediction, with a rate of 77%, 70% recall, and 72% F1 score. Moreover, the study highlighted that two out of five students experience mild depression, around 90% of depressed students do not seek treatment, obese students are 2.5 times more prone to depression, male students are twice as likely to be obese, and male students generally have a higher body mass index (BMI) compared to female students. The study concludes that integrating ML models into the triggers that lead to depression among students.

Author Biographies

Orlando Iparraguirre-Villanueva, Universidad Tecnológica del Perú

Systems Engineer with a Master's Degree in Information Technology Management, PhD in Systems Engineering from Universidad Nacional Federico Villarreal - Peru. ITIL® Foundation Certificate in IT Service, Specialization in Business Continuity Management, Scrum Fundamentals Certification (SFC). National and international speaker/panelist (Panama, Colombia, Ecuador, Venezuela, Mexico). Undergraduate and postgraduate teacher in different universities in the country. Advisor and jury of thesis in different universities. Consultant in information technologies in public and private institutions. Coordinator, director in different private institutions. Specialist in software development, IoT, Business Intelligence, open source software, Augmented Reality, Machine Learning, text mining and virtual environments.

Cleoge Paulino-Moreno, Universidad Católica de Trujillo, Trujillo, Perú

A Systems Engineer with a master’s degree in educational informatics and information technology from the Universidad Catolica de Trujillo - Peru. With a diploma in Public Management, work performed in the educational sector in the IT area.

Andrés Epifanía-Huerta, Universidad Tecnológica del Perú

Systems Engineer with a master’s degree in engineering, a mention in Information and Communication Technologies, with a second professional career in Education with a mention in Computer Science and Informatics, Diploma in Educational Informatics, Specialty Studies in Evaluation of Distance Education Programs, National and International Lecturer in management of business and educational technologies, Experience in University Management.

Carmen Torres-Ceclén, Universidad Católica los Ángeles de Chimbote

I am a Computer and Systems Engineer with a Master's Degree in Computer and Systems with a mention in Information and Communication Technologies and a Doctorate in Education, a diploma in university teaching, certified by EXIN in ITIL, with a high sense of responsibility, high degree of creativity, enterprising and easy adaptation to changes. I am a sociable, communicative person with initiative. I have solid knowledge in the IT field, and I am up-to-date with the latest information technologies.

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Published

2024-08-02

How to Cite

Iparraguirre-Villanueva, O., Paulino-Moreno, C., Epifanía-Huerta, A., & Torres-Ceclén, C. (2024). Machine Learning Models to Classify and Predict Depression in College Students. International Journal of Interactive Mobile Technologies (iJIM), 18(14), pp. 148–163. https://doi.org/10.3991/ijim.v18i14.48669

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Section

Papers