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. 2024 Jan 11:2023:718-725.
eCollection 2023.

Towards a Machine Learning Empowered Prognostic Model for Predicting Disease Progression for Amyotrophic Lateral Sclerosis

Hamza Turabieh  1 , Askar S Afshar  1 , Jeffery Statland  2 , Xing Song  1 ; Pooled Resource Open-Access ALS Clinical Trials Consortium*
Affiliations

Towards a Machine Learning Empowered Prognostic Model for Predicting Disease Progression for Amyotrophic Lateral Sclerosis

Hamza Turabieh et al. AMIA Annu Symp Proc. .

Abstract

Amyotrophic lateral sclerosis (ALS) is a rare and devastating neurodegenerative disorder that is highly heterogeneous and invariably fatal. Due to the unpredictable nature of its progression, accurate tools and algorithms are needed to predict disease progression and improve patient care. To address this need, we developed and compared an extensive set of screener-learner machine learning models to accurately predict the ALS Function-Rating-Scale (ALSFRS) score reduction between 3 and 12 months, by paring 5 state-of-arts feature selection algorithms with 17 predictive models and 4 ensemble models using the publicly available Pooled Open Access Clinical Trials Database (PRO-ACT). Our experiment showed promising results with the blender-type ensemble model achieving the best prediction accuracy and highest prognostic potential.

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Figures

Figure 1.
Figure 1.
Raw clinical features and biomarkers in PRO-ACT
Figure 2.
Figure 2.
Experimental Design.
Figure 3.
Figure 3.
ALSFRS slope distributio
Figure 4.
Figure 4.
Selected features from each algorithm.
Figure 6.
Figure 6.
Kaplan-Meier curves for predicted fast and slow progressor based individual gbr model.
Figure 8a.
Figure 8a.
SHAP value for gbr model with selected features from Boruta algorithm.
Figure 8b.
Figure 8b.
SHAP Value analysis for the small blend ensemble model.
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References

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