Can Machine Learning Predict Mortality in Myocardial Infarction Patients within Several Hours of Hospitalization? A Comparative Analysis

Farah, Christopher; Adla, Yasmine Abu; Awad, Mariette

doi:10.1109/melecon53508.2022.9842984

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…RA-MAP and MI are representative benchmarks of usual medical cohorts presenting a limited number of samples, noise, and a high number of features. They both address tasks of prime importance ( Nielsen et al 2012 , Martin-Gutierrez et al 2022 , Farah et al 2022 , Oliveira et al 2023 ). To ensure the robustness of our conclusions, we report results for four different seeds generating four different training and test splits on each dataset.…”

Section: Methodsmentioning

confidence: 99%

Improving the performance and interpretability on medical datasets using graphical ensemble feature selection

Battistella,

Ghiassian,

Barabási

2024

Bioinformatics

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Motivation A major hindrance towards using Machine Learning on medical datasets is the discrepancy between a large number of variables and small sample sizes. While multiple feature selection techniques have been proposed to avoid the resulting overfitting, overall ensemble techniques offer the best selection robustness. Yet, current methods designed to combine different algorithms generally fail to leverage the dependencies identified by their components. Here, we propose Graphical Ensembling (GE), a graph-theory-based ensemble feature selection technique designed to improve the stability and relevance of the selected features. Results Relying on four datasets, we show that GE increases classification performance with fewer selected features. For example, on rheumatoid arthritis patient stratification, GE outperforms the baseline methods by 9% Balanced Accuracy while relying on fewer features. We use data on sub-cellular networks to show that the selected features (proteins) are closer to the known disease genes, and the uncovered biological mechanisms are more diversified. By successfully tackling the complex correlations between biological variables, we anticipate that GE will improve the medical applications of machine learning. Availability and Implementation https://github.com/ebattistella/auto_machine_learning

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