Affiliations: [a] Artificial Intelligence Lab., National Technological, Misantla Campus, Mexico
| [b]
UCSI University, Malaysia
| [c]
University of Baja California, Mexico
Correspondence:
[*]
Corresponding author. Pozos-Parra, P., University of Baja California, Mexico. E-mail: maria.pozos@uabc.edu.mx.
Abstract: Background:Many neurodegenerative diseases affect human gait. Gait analysis is an example of a non-invasive manner to diagnose these diseases. Nevertheless, gait analysis is difficult to do because patients with different neurodegenerative diseases may have similar human gaits. Machine learning algorithms may improve the correct identification of these pathologies. However, the problem with many classification algorithms is a lack of transparency and interpretability for the final user. Methods:In this study, we implemented the PS-Merge operator for the classification, employing gait biomarkers of a public dataset. Results:The highest classification percentage was 83.77%, which means an acceptable degree of reliability. Conclusions:Our results show that PS-Merge has the ability to explain how the algorithm chooses an option, i.e., the operator can be seen as a first step to obtaining an eXplainable Artificial Intelligence (XAI).
