# Different Algorithms (Might) Uncover Different Patterns: A Brain-Age Prediction Case Study

arXiv: https://arxiv.org/abs/2402.09464 · HTML: https://mlsanity.com/paper/2402.09464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree Ensembles](https://mlsanity.com/paper/2010.13972/) (2020)
- [Interpretable and Robust AI in EEG Systems: A Survey](https://mlsanity.com/paper/2304.10755/) (2023)
- [Feature Imitating Networks](https://mlsanity.com/paper/2110.04831/) (2021)
- [The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains](https://mlsanity.com/paper/2309.12279/) (2023)

## Abstract (arXiv)

Machine learning is a rapidly evolving field with a wide range of applications, including biological signal analysis, where novel algorithms often improve the state-of-the-art. However, robustness to algorithmic variability - measured by different algorithms, consistently uncovering similar findings - is seldom explored. In this paper we investigate whether established hypotheses in brain-age prediction from EEG research validate across algorithms. First, we surveyed literature and identified various features known to be informative for brain-age prediction. We employed diverse feature extraction techniques, processing steps, and models, and utilized the interpretative power of SHapley Additive exPlanations (SHAP) values to align our findings with the existing research in the field. Few of our models achieved state-of-the-art performance on the specific data-set we utilized. Moreover, analysis demonstrated that while most models do uncover similar patterns in the EEG signals, some variability could still be observed. Finally, a few prominent findings could only be validated using specific models. We conclude by suggesting remedies to the potential implications of this lack of robustness to model variability.