Identification of post-COVID-19 symptoms using brain structural MRI features: a machine learning approach
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915527447805952 |
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| author | Reza, Abdi |
| author_facet | Reza, Abdi |
| contents | Identifying long COVID symptoms is a challenging task, primarily due to the reliance on patient reports and the lack of disease specific biomarkers. The objective of this study is to identify individual long COVID symptoms, post COVID 19 conditions (PCC) participants, and participants' sex, and to identify the associated brain regions by developing an explainable machine learning algorithm using brain MRI features. This study implements secondary analysis using an anonymized, publicly accessible dataset that categorizes participants into three groups: the PCC group, the Unimpaired Post COVID 19 group (UPC), and the Healthy Non COVID group (HNC), each with corresponding symptoms, demographics, and brain structural MRI features. The aim is to develop and cross validate a support vector classifier (SVC) algorithm to identify the occurrence of various target labels from the dataset. The SVC classifier identified the occurrence of long-COVID symptoms with various performances for different target labels. The model performance and influential area are identified and discussed in light of previous research. The demonstrated approach offers an alternative modality for determining the occurrence of long COVID symptoms based on neuroimaging biomarkers. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_00195 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identification of post-COVID-19 symptoms using brain structural MRI features: a machine learning approach Reza, Abdi Other Quantitative Biology Identifying long COVID symptoms is a challenging task, primarily due to the reliance on patient reports and the lack of disease specific biomarkers. The objective of this study is to identify individual long COVID symptoms, post COVID 19 conditions (PCC) participants, and participants' sex, and to identify the associated brain regions by developing an explainable machine learning algorithm using brain MRI features. This study implements secondary analysis using an anonymized, publicly accessible dataset that categorizes participants into three groups: the PCC group, the Unimpaired Post COVID 19 group (UPC), and the Healthy Non COVID group (HNC), each with corresponding symptoms, demographics, and brain structural MRI features. The aim is to develop and cross validate a support vector classifier (SVC) algorithm to identify the occurrence of various target labels from the dataset. The SVC classifier identified the occurrence of long-COVID symptoms with various performances for different target labels. The model performance and influential area are identified and discussed in light of previous research. The demonstrated approach offers an alternative modality for determining the occurrence of long COVID symptoms based on neuroimaging biomarkers. |
| title | Identification of post-COVID-19 symptoms using brain structural MRI features: a machine learning approach |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2510.00195 |