Predicting Disease Progression of Amyotrophic Lateral Sclerosis Using Feed-Forward Neural Networks and LSTM

Deepa Venna, Aaryasri Polagani, Pranavi Sowreddy

2025

Abstract

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease marked by the decline in motor function, and accurate disease progression prediction is crucial for effective treatment planning. This study presents a hybrid deep learning model that combines a feedforward neural network (FFNN) with a long short-term memory (LSTM) network to predict ALS progression, measured through the ALS Functional Rating Scale-Revised (ALSFRS-R) scores. Using ALSFRS-R scores from 3 and 12 months alongside Riluzole treatment data, the model calculates the decline rate, reflecting ALS progression. The FFNN processes static features such as patient demographics and treatment data, while the LSTM captures temporal trends in ALSFRS-R scores. Training and evaluation were conducted on ALS clinical data using root mean squared error (RMSE) and Pearson correlation coefficient (PCC) to assess predictive accuracy and the strength of correlation with actual progression. Results show that including Riluzole improves predictive accuracy, offering insights into its impact on ALS progression.

Paper Citation

in Harvard Style

Venna D., Polagani A. and Sowreddy P. (2025). Predicting Disease Progression of Amyotrophic Lateral Sclerosis Using Feed-Forward Neural Networks and LSTM. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 789-795. DOI: 10.5220/0013602600004664

in Bibtex Style

@conference{incoft25,
author={Deepa Venna and Aaryasri Polagani and Pranavi Sowreddy},
title={Predicting Disease Progression of Amyotrophic Lateral Sclerosis Using Feed-Forward Neural Networks and LSTM},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={789-795},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013602600004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Predicting Disease Progression of Amyotrophic Lateral Sclerosis Using Feed-Forward Neural Networks and LSTM
SN - 978-989-758-763-4
AU - Venna D.
AU - Polagani A.
AU - Sowreddy P.
PY - 2025
SP - 789
EP - 795
DO - 10.5220/0013602600004664
PB - SciTePress