Prediction of Depression via Supervised Learning Models: Performance Comparison and Analysis
DOI:
https://doi.org/10.3991/ijoe.v19i09.39823Keywords:
Machine learning, K-NN, DT, SVM, NB, LR , DepressionAbstract
This document Among all the various types of mental and psychosocial illnesses, the most commonly occurring type is depression. It can cause serious problems such as suicide. Therefore, early detection is important to stop the progression of this disease that could endanger human lives. Predicting and detecting early-stage depression using machine learning (ML) techniques is a promising strategy. This study’s main purpose is to assess which ML techniques are highly appropriate and accurate regarding such diagnoses. Six supervised ML techniques namely: K-nearest neighbor (KNN), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Support vector machine (SVM) and Naive Bayes (NB) were applied on dataset collected from Kaggle and compared for their accuracy (ACC) and performance in predicting depression. The performance of each model was evaluated using 10-fold cross-validation and evaluated in terms of ACC, F1-score, Precision (PR), and Sensitivity (SEN). Based on the experimental results analysis, we can conclude that SVM and LR performed better than all other methods with an ACC of 83,32%. Therefore, we found that a simple ML algorithm can be used to assist clinicians and practitioners predict depression at an early stage, with excellent potential utility and a considerable degree of ACC.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 zineb Sabouri, Noreddine Gherabi, Mohammed Nasri, Mohamed Amnai, Hakim El Massari, Imane Moustati
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.