  Amyotrophic Lateral Sclerosis (ALS) is characterized as a rapidly progressive
neurodegenerative disease that presents individuals with limited treatment
options in the realm of medical interventions and therapies. The disease
showcases a diverse range of onset patterns and progression trajectories,
emphasizing the critical importance of early detection of functional decline to
enable tailored care strategies and timely therapeutic interventions. The
present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on
utilizing sensor-derived data obtained through an app. This data is used to
construct various machine learning models specifically designed to forecast the
advancement of the ALS Functional Rating Scale-Revised (ALSFRS-R) score,
leveraging the dataset provided by the organizers. In our analysis, multiple
predictive models were evaluated to determine their efficacy in handling ALS
sensor data. The temporal aspect of the sensor data was compressed and
amalgamated using statistical methods, thereby augmenting the interpretability
and applicability of the gathered information for predictive modeling
objectives. The models that demonstrated optimal performance were a naive
baseline and ElasticNet regression. The naive model achieved a Mean Absolute
Error (MAE) of 0.20 and a Root Mean Square Error (RMSE) of 0.49, slightly
outperforming the ElasticNet model, which recorded an MAE of 0.22 and an RMSE
of 0.50. Our comparative analysis suggests that while the naive approach
yielded marginally better predictive accuracy, the ElasticNet model provides a
robust framework for understanding feature contributions.
