Combining Neighbor Models to Improve Predictions of Age of Onset of ATTRv Carriers

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Maria Pedroto (Author) FCUP; Jorge, AM (Author) FCUP; João Mendes-Moreira (Author) FEUP; Coelho, T (Author) Other

Conference proceedings International

Title: Progress in Artificial Intelligence - 22nd EPIA Conference on Artificial Intelligence, EPIA 2023, Faial Island, Azores, September 5-8, 2023, Proceedings, Part II

Pages: 286-297

22nd EPIA Conference on Artificial Intelligence, EPIA 2023

Faial Island, 5 September 2023 through 8 September 2023

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Other information

Authenticus ID: P-00Z-KXZ

DOI: 10.1007/978-3-031-49011-8_23

Abstract (EN): Transthyretin (TTR)-related familial amyloid polyneuropathy (ATTRv) is a life-threatening autosomal dominant disease and the age of onset represents the moment when first symptoms are felt. Accurately predicting the age of onset for a given patient is relevant for risk assessment and treatment management. In this work, we evaluate the impact of combining prediction models obtained from neighboring time windows on prediction error. We propose Symmetric (Sym) and Asymmetric (Asym) models which represent two different averaging approaches. These are incorporated with a weighting mechanism as to create Symmetric (Sym), Symmetric-weighted (Sym-w), Asymmetric (Asym), and Asymmetric-weighted (Asym-w). These four ensemble models are then compared to the original approach which is focused on individual regression base learners namely: Baseline (BL), Decision Tree (DT), Elastic Net (EN), Lasso (LA), Linear Regression (LR), Random Forest (RF), Ridge (RI), Support Vector Regressor (SV) and XGBoost (XG). Our results show that by aggregating predictions from neighbor models the average mean absolute error obtained by each base learner decreases. Overall, the best results are achieved by regression-based ensemble tree models as base learners.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12