Benchmarking of Classification Algorithms for Psychological Diagnosis

Jhony Llano; Vanessa Ramirez; Paulina Morillo

doi:10.1007/978-3-030-46785-2_16

Benchmarking of Classification Algorithms for Psychological Diagnosis

Jhony Llano, Vanessa Ramirez, Paulina Morillo

Multimedia Educational Innovation Group for the Teaching of Technical Subjects Gie-Mm4Tech (GIE-MM4TECH)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Smart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings
Editors	Fabián R. Narváez, Diego F. Vallejo, Paulina A. Morillo, Julio R. Proaño
Publisher	Springer
Pages	188-201
Number of pages	14
ISBN (Print)	9783030467845
DOIs	https://doi.org/10.1007/978-3-030-46785-2_16
State	Published - 1 Jan 2020
Event	1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019 - Quito, Ecuador Duration: 2 Dec 2019 → 4 Dec 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1154 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019
Country/Territory	Ecuador
City	Quito
Period	2/12/19 → 4/12/19

Bibliographical note

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-46785-2_16

Cite this

Llano, J., Ramirez, V., & Morillo, P. (2020). Benchmarking of Classification Algorithms for Psychological Diagnosis. In F. R. Narváez, D. F. Vallejo, P. A. Morillo, & J. R. Proaño (Eds.), Smart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings (pp. 188-201). (Communications in Computer and Information Science; Vol. 1154 CCIS). Springer. https://doi.org/10.1007/978-3-030-46785-2_16

Llano, Jhony ; Ramirez, Vanessa ; Morillo, Paulina. / Benchmarking of Classification Algorithms for Psychological Diagnosis. Smart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings. editor / Fabián R. Narváez ; Diego F. Vallejo ; Paulina A. Morillo ; Julio R. Proaño. Springer, 2020. pp. 188-201 (Communications in Computer and Information Science).

@inproceedings{973aba94e27149269c0149b1a2ae0508,

title = "Benchmarking of Classification Algorithms for Psychological Diagnosis",

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.",

keywords = "Accuracy, Bayesian Networks, k-Nearest Neighbors, Logistic Regression, Performance, Precision, Random Forest, Recall, ROC, Support Vector Machine",

author = "Jhony Llano and Vanessa Ramirez and Paulina Morillo",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019 ; Conference date: 02-12-2019 Through 04-12-2019",

year = "2020",

month = jan,

day = "1",

doi = "10.1007/978-3-030-46785-2_16",

language = "English",

isbn = "9783030467845",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "188--201",

editor = "Narv{\'a}ez, {Fabi{\'a}n R.} and Vallejo, {Diego F.} and Morillo, {Paulina A.} and Proa{\~n}o, {Julio R.}",

booktitle = "Smart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings",

}

Llano, J, Ramirez, V & Morillo, P 2020, Benchmarking of Classification Algorithms for Psychological Diagnosis. in FR Narváez, DF Vallejo, PA Morillo & JR Proaño (eds), Smart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings. Communications in Computer and Information Science, vol. 1154 CCIS, Springer, pp. 188-201, 1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019, Quito, Ecuador, 2/12/19. https://doi.org/10.1007/978-3-030-46785-2_16

Benchmarking of Classification Algorithms for Psychological Diagnosis. / Llano, Jhony; Ramirez, Vanessa; Morillo, Paulina.
Smart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings. ed. / Fabián R. Narváez; Diego F. Vallejo; Paulina A. Morillo; Julio R. Proaño. Springer, 2020. p. 188-201 (Communications in Computer and Information Science; Vol. 1154 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Benchmarking of Classification Algorithms for Psychological Diagnosis

AU - Llano, Jhony

AU - Ramirez, Vanessa

AU - Morillo, Paulina

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Accuracy

KW - Bayesian Networks

KW - k-Nearest Neighbors

KW - Logistic Regression

KW - Performance

KW - Precision

KW - Random Forest

KW - Recall

KW - ROC

KW - Support Vector Machine

UR - http://www.scopus.com/inward/record.url?scp=85084822488&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/11557e69-95c3-3cbe-bb49-b015ab94d26f/

U2 - 10.1007/978-3-030-46785-2_16

DO - 10.1007/978-3-030-46785-2_16

M3 - Conference contribution

AN - SCOPUS:85084822488

SN - 9783030467845

T3 - Communications in Computer and Information Science

SP - 188

EP - 201

BT - Smart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings

A2 - Narváez, Fabián R.

A2 - Vallejo, Diego F.

A2 - Morillo, Paulina A.

A2 - Proaño, Julio R.

PB - Springer

T2 - 1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019

Y2 - 2 December 2019 through 4 December 2019

ER -

Llano J, Ramirez V, Morillo P. Benchmarking of Classification Algorithms for Psychological Diagnosis. In Narváez FR, Vallejo DF, Morillo PA, Proaño JR, editors, Smart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings. Springer. 2020. p. 188-201. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-46785-2_16

Benchmarking of Classification Algorithms for Psychological Diagnosis

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this