# Machine Learning Driven Biomarker Selection for Medical Diagnosis

arXiv: https://arxiv.org/abs/2405.10345 · HTML: https://mlsanity.com/paper/2405.10345/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 3 of 3)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [A predictive analytics approach for stroke prediction using machine learning and neural networks](https://mlsanity.com/paper/2203.00497/) (2022)

## Built on by (most cited first, 1 of 1)

- [Quantum Reservoir Computing with Neutral Atoms on a Small, Complex, Medical Dataset](https://mlsanity.com/paper/2602.14641/) (2026)

## Abstract (arXiv)

Recent advances in experimental methods have enabled researchers to collect data on thousands of analytes simultaneously. This has led to correlational studies that associated molecular measurements with diseases such as Alzheimer's, Liver, and Gastric Cancer. However, the use of thousands of biomarkers selected from the analytes is not practical for real-world medical diagnosis and is likely undesirable due to potentially formed spurious correlations. In this study, we evaluate 4 different methods for biomarker selection and 4 different machine learning (ML) classifiers for identifying correlations, evaluating 16 approaches in all. We found that contemporary methods outperform previously reported logistic regression in cases where 3 and 10 biomarkers are permitted. When specificity is fixed at 0.9, ML approaches produced a sensitivity of 0.240 (3 biomarkers) and 0.520 (10 biomarkers), while standard logistic regression provided a sensitivity of 0.000 (3 biomarkers) and 0.040 (10 biomarkers). We also noted that causal-based methods for biomarker selection proved to be the most performant when fewer biomarkers were permitted, while univariate feature selection was the most performant when a greater number of biomarkers were permitted.