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.
|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|
|Number of pages||14|
|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
|Name||Communications in Computer and Information Science|
|Conference||1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019|
|Period||2/12/19 → 4/12/19|
Bibliographical noteFunding Information:
This work was supported by IDEIAGEOCA Research Group of the Universidad Politécnica Salesiana.
© Springer Nature Switzerland AG 2020.
- Bayesian Networks
- k-Nearest Neighbors
- Logistic Regression
- Random Forest
- Support Vector Machine