Handling Uncertainty in Health Data using Generative Algorithms
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929742943354880 |
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| author | Loodaricheh, Mahdi Arab Majmudar, Neh Raja, Anita Salleb-Aouissi, Ansaf |
| author_facet | Loodaricheh, Mahdi Arab Majmudar, Neh Raja, Anita Salleb-Aouissi, Ansaf |
| contents | Understanding and managing uncertainty is crucial in machine learning, especially in high-stakes domains like healthcare, where class imbalance can impact predictions. This paper introduces RIGA, a novel pipeline that mitigates class imbalance using generative AI. By converting tabular healthcare data into images, RIGA leverages models like cGAN, VQVAE, and VQGAN to generate balanced samples, improving classification performance. These representations are processed by CNNs and later transformed back into tabular format for seamless integration. This approach enhances traditional classifiers like XGBoost, improves Bayesian structure learning, and strengthens ML model robustness by generating realistic synthetic data for underrepresented classes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03715 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Handling Uncertainty in Health Data using Generative Algorithms Loodaricheh, Mahdi Arab Majmudar, Neh Raja, Anita Salleb-Aouissi, Ansaf Machine Learning Understanding and managing uncertainty is crucial in machine learning, especially in high-stakes domains like healthcare, where class imbalance can impact predictions. This paper introduces RIGA, a novel pipeline that mitigates class imbalance using generative AI. By converting tabular healthcare data into images, RIGA leverages models like cGAN, VQVAE, and VQGAN to generate balanced samples, improving classification performance. These representations are processed by CNNs and later transformed back into tabular format for seamless integration. This approach enhances traditional classifiers like XGBoost, improves Bayesian structure learning, and strengthens ML model robustness by generating realistic synthetic data for underrepresented classes. |
| title | Handling Uncertainty in Health Data using Generative Algorithms |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.03715 |