Benchmarking of Classification Algorithms for Psychological Diagnosis

Jhony Llano, Vanessa Ramirez, Paulina Morillo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Generating a clinical diagnosis of a mental disorder is a complex process due to the variety of biological factors that affect this type of condition, so it is necessary that a professional performs a deep evaluation in order to identify and determine the type of disorder that affects the patient. This paper proposes the implementation and comparison of five machine learning algorithms (ML) to generate automatic diagnoses of mental disorders, through the set of symptoms present in a patient. The algorithms selected for comparison are: Support Vector Machine, Logistic Regression, Random Forest, Bayesian Networks, k-Nearest Neighbors (k-NN). The evaluation metrics used on the benchmarked were precision, accuracy, recall, error rate and also we analyzed the ROC curves and the AUC values. The general results show that the Logistic Regression algorithm obtained a better performance with 70.82% of accuracy. The Support Vector Machine model, on the other hand, showed a low performance reaching only 42.99% accuracy.

Original languageEnglish
Title of host publicationSmart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings
EditorsFabián R. Narváez, Diego F. Vallejo, Paulina A. Morillo, Julio R. Proaño
PublisherSpringer
Pages188-201
Number of pages14
ISBN (Print)9783030467845
DOIs
StatePublished - 1 Jan 2020
Event1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019 - Quito, Ecuador
Duration: 2 Dec 20194 Dec 2019

Publication series

NameCommunications in Computer and Information Science
Volume1154 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019
Country/TerritoryEcuador
CityQuito
Period2/12/194/12/19

Bibliographical note

Funding Information:
This work was supported by IDEIAGEOCA Research Group of the Universidad Politécnica Salesiana.

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

  • Accuracy
  • Bayesian Networks
  • k-Nearest Neighbors
  • Logistic Regression
  • Performance
  • Precision
  • Random Forest
  • Recall
  • ROC
  • Support Vector Machine

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