Benchmarking of Classification Algorithms for Psychological Diagnosis

Jhony Llano, Vanessa Ramirez, Paulina Morillo

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaSmart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings
EditoresFabián R. Narváez, Diego F. Vallejo, Paulina A. Morillo, Julio R. Proaño
EditorialSpringer
Páginas188-201
Número de páginas14
ISBN (versión impresa)9783030467845
DOI
EstadoPublicada - 1 ene 2020
Evento1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019 - Quito, Ecuador
Duración: 2 dic 20194 dic 2019

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1154 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

Conferencia1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019
País/TerritorioEcuador
CiudadQuito
Período2/12/194/12/19

Nota bibliográfica

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

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