‘Machine Learning’ multiclassification for stage diagnosis of Alzheimer’s disease utilizing augmented blood gene expression and feature fusion

Topic: Amyloid, tau and blood-test diagnosis

Three blood gene expression datasets of high dimensionality and low sample size (HDLSS) have been utilized in this study, with one dataset exhibiting significant class imbalance. This study integrates clinical data from electronic health records (EHRs) with gene expression datasets, which has been found to significantly enhance the accuracy of stage diagnosis. Furthermore, our analysis of two integrated NCBI datasets revealed 125 gene transcripts demonstrating superior effectiveness among more than 30,000 potential candidates. These findings resulted in the development of two distinct model categories: one derived from the ADNI dataset and the other from the integrated NCBI dataset. Result In the case of ADNI study participants, we obtained best multi-classification performance with ‘ROC AUC’ scores of 0. 76, 0.76, 0.71 for the CN, MCI, and Dementia stages, respectively. When assessed with GSE3061 test data, the model achieved optimal AUC scores of 0.81, 0.75, and 0.78, and F1 scores of 0.74, 0.67, and 0.73.This research identified MAPK14, MID1, TEP1, PLG, DRAXIN, USP47 as genes associated with AD. Conclusion Using machine learning multiclassification techniques on blood gene expression profile data from ADNI and NCBI, we achieved the most promising results to date for diagnosing multiple stages of Alzheimer’s disease. This proves that the efficacy of our feature selection techniques that could find essential genes associated with AD.

Key findings

• Three blood gene expression datasets of high dimensionality and low sample size (HDLSS) have been utilized in this study, with one dataset exhibiting significant class imbalance.
• This study integrates clinical data from electronic health records (EHRs) with gene expression datasets, which has been found to significantly enhance the accuracy of stage diagnosis.
• Furthermore, our analysis of two integrated NCBI datasets revealed 125 gene transcripts demonstrating superior effectiveness among more than 30,000 potential candidates.
• These findings resulted in the development of two distinct model categories: one derived from the ADNI dataset and the other from the integrated NCBI dataset.
• Result In the case of ADNI study participants, we obtained best multi-classification performance with ‘ROC AUC’ scores of 0. 76, 0.76, 0.71 for the CN, MCI, and Dementia stages, respectively.

Why it matters

Machine learning multiclassification using blood gene expression data integrated with clinical records achieved promising results for diagnosing multiple stages of Alzheimer's disease, with AUC scores of 0.76, 0.76, and 0.71 for cognitively normal, mild cognitive impairment, and dementia stages in the ADNI dataset. This is a diagnostic approach and does not treat, cure, or prevent Alzheimer's disease.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.1007/s42452-025-07237-1
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• Free full text: Discover Applied Sciences →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{sarma2025-e06d316e,
  author  = {Sarma, Manash and Chatterjee, Subarna},
  title   = {{‘Machine Learning’ multiclassification for stage diagnosis of Alzheimer’s disease utilizing augmented blood gene expression and feature fusion}},
  journal = {Discover Applied Sciences},
  year    = {2025},
  doi     = {10.1007/s42452-025-07237-1},
  url     = {https://doi.org/10.1007/s42452-025-07237-1}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Rare genetic variation in fibronectin 1 (FN1) protects against APOEε4 in Alzheimer's disease Acta Neuropathologica · 2024-04-10 · usefulness score 72/100 · Genetics
• 2024 Alzheimer's disease facts and figures Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024-04-30 · usefulness score 62/100 · Caregiving
• The genetics of Alzheimer disease Cold Spring Harbor perspectives in medicine · 2012-10-01 · usefulness score 51/100 · Genetics

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