Neurological Disease Classification Based on Gait Analysis Through Transformation-Based Multiple Linear Regression Normalization

• Type: Article in International Conference Proceedings Book
• Date: 2025

Authors

Barrios, J (Author) Other; Araújo, B (Author) Other; Gago, M (Author) Other; Erlhagen, W (Author) Other; Bicho, E (Author) Other; Ferreira, F (Author) FEP

Conference proceedings International

Title: Springer Proceedings in Mathematics and Statistics

Pages: 381-392

26th Congress of the Portuguese Statistical Society, SPE 2023

Evora, 13 October 2021 through 16 October 2021

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-017-SYJ

DOI: 10.1007/978-3-031-68949-9_28

Abstract (EN): Gait analysis plays a vital role in clinical assessments by providing clear, objective insights into how diseases progress, how impairments in walking are manifested, and the effectiveness of various treatments. Our study tackled the challenge of comparing individuals by using multiple linear regression models to account for personal physical differences. We also looked at improving these models by transforming variables. Our focus was on individuals with Parkinson¿s disease, normal pressure hydrocephalus, and a control group without these conditions. We used statistical tests to select relevant features and reduced the number of variables using principal component analysis. Techniques like Random Forest, Support Vector Machine, and Multiple Linear Perceptron were used to understand the normalized data patterns. Additionally, SHapley¿s Additive exPlanations method helped us identify which variables were most influential before and after data normalization. This work opens new possibilities for using these techniques in both clinical settings and further research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11