Early detection of neurodegenerative diseases based on Moroccan population gait analysis

Abstract

Neurodegenerative disorders (NDD), such as Alzheimer's disease (AD), Parkinson's disease (PD) and Multiple Sclerosis (MS), poses significant challenges to global public health due to their progressive impact on motor and cognitive functions. With the global increase in life expectancy, these diseases are becoming more prevalent, leading to an urgent need for effective prevention, early diagnosis, and treatment strategies.Walking is a fundamental aspect of human mobility and is intricately linked to neurological health. Changes in gait patterns often occur in the advanced stages of neurodegenerative diseases, making them potential biomarkers for early detection and monitoring.This study aims to analyze gait patterns in a collected dataset of a Moroccan population, in order to classify gait models using the GAITRite system. By extracting gait parameters and employing machine learning (ML) techniques, we seek to identify distinct gait patterns associated with different neurodegenerative disorders. The data is conducted at the Neurological Service of University Hospital Center Hassan II of Fez, Morocco.Ultimately, this research aims to enhance early detection and improve outcomes for individuals affected by neurodegenerative diseases, thereby contributing to increased life expectancy and improved quality of life.