Detection of Alzheimer’s Disease using Explainable Machine Learning and Mathematical Models
Purpose: This study proposes a novel approach combining mathematical modeling and machine learning (ML) to classify four Alzheimer’s disease (AD) stages from magnetic resonance imaging (MRI) scans. Methodology: We first mapped each MRI pixel value matrix to a 2 × 2 matrix, using the techniques of fo...
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer Medknow Publications
2025-01-01
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| Series: | Journal of Medical Physics |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/jmp.jmp_128_24 |
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| _version_ | 1849768894790631424 |
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| author | Krishna Mahapatra R. Selvakumar |
| author_facet | Krishna Mahapatra R. Selvakumar |
| author_sort | Krishna Mahapatra |
| collection | DOAJ |
| description | Purpose:
This study proposes a novel approach combining mathematical modeling and machine learning (ML) to classify four Alzheimer’s disease (AD) stages from magnetic resonance imaging (MRI) scans.
Methodology:
We first mapped each MRI pixel value matrix to a 2 × 2 matrix, using the techniques of forming a moment of inertia (MI) tensor, commonly used in physics to measure the mass distribution. Using the properties of the obtained inertia tensor and their eigenvalues, along with ML techniques, we classify the different stages of AD.
Results:
In this study, we have compared the performance of an intuitive mathematical model integrated with a machine learning approach across various ML models. Among them, the Gaussian Naïve Bayes classifier achieves the highest accuracy of 95.45%.
Conclusions:
Beyond improved accuracy, our method offers potential for computational efficiency due to dimensionality reduction and provides novel physical insights into AD through inertia tensor analysis. |
| format | Article |
| id | doaj-art-4ebc3669b1b3481b96d19ae6525866eb |
| institution | DOAJ |
| issn | 0971-6203 1998-3913 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Medical Physics |
| spelling | doaj-art-4ebc3669b1b3481b96d19ae6525866eb2025-08-20T03:03:40ZengWolters Kluwer Medknow PublicationsJournal of Medical Physics0971-62031998-39132025-01-0150113113910.4103/jmp.jmp_128_24Detection of Alzheimer’s Disease using Explainable Machine Learning and Mathematical ModelsKrishna MahapatraR. SelvakumarPurpose: This study proposes a novel approach combining mathematical modeling and machine learning (ML) to classify four Alzheimer’s disease (AD) stages from magnetic resonance imaging (MRI) scans. Methodology: We first mapped each MRI pixel value matrix to a 2 × 2 matrix, using the techniques of forming a moment of inertia (MI) tensor, commonly used in physics to measure the mass distribution. Using the properties of the obtained inertia tensor and their eigenvalues, along with ML techniques, we classify the different stages of AD. Results: In this study, we have compared the performance of an intuitive mathematical model integrated with a machine learning approach across various ML models. Among them, the Gaussian Naïve Bayes classifier achieves the highest accuracy of 95.45%. Conclusions: Beyond improved accuracy, our method offers potential for computational efficiency due to dimensionality reduction and provides novel physical insights into AD through inertia tensor analysis.https://journals.lww.com/10.4103/jmp.jmp_128_24alzheimer’s diseasedimensionality reductionmachine learningmagnetic resonance imagingmathematical modelingmoment of inertia tensorprincipal component analysis |
| spellingShingle | Krishna Mahapatra R. Selvakumar Detection of Alzheimer’s Disease using Explainable Machine Learning and Mathematical Models Journal of Medical Physics alzheimer’s disease dimensionality reduction machine learning magnetic resonance imaging mathematical modeling moment of inertia tensor principal component analysis |
| title | Detection of Alzheimer’s Disease using Explainable Machine Learning and Mathematical Models |
| title_full | Detection of Alzheimer’s Disease using Explainable Machine Learning and Mathematical Models |
| title_fullStr | Detection of Alzheimer’s Disease using Explainable Machine Learning and Mathematical Models |
| title_full_unstemmed | Detection of Alzheimer’s Disease using Explainable Machine Learning and Mathematical Models |
| title_short | Detection of Alzheimer’s Disease using Explainable Machine Learning and Mathematical Models |
| title_sort | detection of alzheimer s disease using explainable machine learning and mathematical models |
| topic | alzheimer’s disease dimensionality reduction machine learning magnetic resonance imaging mathematical modeling moment of inertia tensor principal component analysis |
| url | https://journals.lww.com/10.4103/jmp.jmp_128_24 |
| work_keys_str_mv | AT krishnamahapatra detectionofalzheimersdiseaseusingexplainablemachinelearningandmathematicalmodels AT rselvakumar detectionofalzheimersdiseaseusingexplainablemachinelearningandmathematicalmodels |