Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data
Alzheimer’s Disease (AD) is a significant global health issue, and the current diagnostic techniques cannot diagnose the disease at its early stages, hence the difficulty of early therapeutic management. In response to the formulated research problem, this study articulates a new multimodal machine-...
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| Формат: | Статья |
| Язык: | английский |
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Elsevier
2025-01-01
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| Серии: | Computer Methods and Programs in Biomedicine Update |
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| Online-ссылка: | http://www.sciencedirect.com/science/article/pii/S2666990025000345 |
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| _version_ | 1839603976772780032 |
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| author | Shehu Mohammed Neha Malhotra |
| author_facet | Shehu Mohammed Neha Malhotra |
| author_sort | Shehu Mohammed |
| collection | DOAJ |
| description | Alzheimer’s Disease (AD) is a significant global health issue, and the current diagnostic techniques cannot diagnose the disease at its early stages, hence the difficulty of early therapeutic management. In response to the formulated research problem, this study articulates a new multimodal machine-learning framework for early AD diagnosis. The main goal is to combine multiple biomarkers: neuroimaging, CSF, genetic, and longitudinal cognitive data and develop a robust model for accurate early AD diagnosis. The importance of this work is in the opportunity to shift diagnostic paradigms by employing deep learning algorithms, including CNNs, LSTM networks, and GNNs to analyze spatial, temporal, and relational patterns across multi-modal data. The methodology involves federated learning and domain adaptation with GANs to integrate data from multiple centers with the patient’s privacy intact. It shows that the proposed multimodal model is superior to single-modality models with an AUC-ROC of 0.94 and reveals that hippocampal volume and plasma p-tau are the most informative biomarkers in the early diagnosis of AD. The study’s implications indicate that combining multimodal data improves diagnostic accuracy and clinical relevance by providing a roadmap to developing personalized medicine and better patient care. Future work will be aimed at increasing the variability of the dataset and the clinical trials to test the model to improve its applicability and performance in actual practice. |
| format | Article |
| id | doaj-art-e8473bcc43cd478ca19dc236d27a24f8 |
| institution | Matheson Library |
| issn | 2666-9900 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computer Methods and Programs in Biomedicine Update |
| spelling | doaj-art-e8473bcc43cd478ca19dc236d27a24f82025-08-02T04:47:54ZengElsevierComputer Methods and Programs in Biomedicine Update2666-99002025-01-018100209Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker dataShehu Mohammed0Neha Malhotra1Corresponding author.; Department of Computer Applications, Lovely Professional University Phagwara, IndiaDepartment of Computer Applications, Lovely Professional University Phagwara, IndiaAlzheimer’s Disease (AD) is a significant global health issue, and the current diagnostic techniques cannot diagnose the disease at its early stages, hence the difficulty of early therapeutic management. In response to the formulated research problem, this study articulates a new multimodal machine-learning framework for early AD diagnosis. The main goal is to combine multiple biomarkers: neuroimaging, CSF, genetic, and longitudinal cognitive data and develop a robust model for accurate early AD diagnosis. The importance of this work is in the opportunity to shift diagnostic paradigms by employing deep learning algorithms, including CNNs, LSTM networks, and GNNs to analyze spatial, temporal, and relational patterns across multi-modal data. The methodology involves federated learning and domain adaptation with GANs to integrate data from multiple centers with the patient’s privacy intact. It shows that the proposed multimodal model is superior to single-modality models with an AUC-ROC of 0.94 and reveals that hippocampal volume and plasma p-tau are the most informative biomarkers in the early diagnosis of AD. The study’s implications indicate that combining multimodal data improves diagnostic accuracy and clinical relevance by providing a roadmap to developing personalized medicine and better patient care. Future work will be aimed at increasing the variability of the dataset and the clinical trials to test the model to improve its applicability and performance in actual practice.http://www.sciencedirect.com/science/article/pii/S2666990025000345Alzheimer's Disease (AD)Early diagnosisMultimodal biomarkersMachine learningDeep learningNeuroimaging |
| spellingShingle | Shehu Mohammed Neha Malhotra Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data Computer Methods and Programs in Biomedicine Update Alzheimer's Disease (AD) Early diagnosis Multimodal biomarkers Machine learning Deep learning Neuroimaging |
| title | Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data |
| title_full | Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data |
| title_fullStr | Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data |
| title_full_unstemmed | Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data |
| title_short | Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data |
| title_sort | predicting alzheimer s disease onset a machine learning framework for early diagnosis using biomarker data |
| topic | Alzheimer's Disease (AD) Early diagnosis Multimodal biomarkers Machine learning Deep learning Neuroimaging |
| url | http://www.sciencedirect.com/science/article/pii/S2666990025000345 |
| work_keys_str_mv | AT shehumohammed predictingalzheimersdiseaseonsetamachinelearningframeworkforearlydiagnosisusingbiomarkerdata AT nehamalhotra predictingalzheimersdiseaseonsetamachinelearningframeworkforearlydiagnosisusingbiomarkerdata |