Data analysis with Shapley values for automatic subject selection in Alzheimer’s disease data sets using interpretable machine learning

Bloch, Louise; Friedrich, Christoph M.; for the Alzheimer’s Disease Neuroimaging Initiative

doi:10.1186/s13195-021-00879-4

Cited by 70Dementia and Cognitive Impairment ResearchArtificial Intelligence in HealthcareExplainable Artificial Intelligence (XAI)

Data analysis with Shapley values for automatic subject selection in Alzheimer’s disease data sets using interpretable machine learning

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Bloch, Louise, Friedrich, Christoph M., for the Alzheimer’s Disease Neuroimaging Initiative·2021·Alzheimer's Research & Therapy

DOI 10.1186/s13195-021-00879-4PMC PMC8444618

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Abstract

Background: For the recruitment and monitoring of subjects for therapy studies, it is important to predict whether mild cognitive impaired (MCI) subjects will prospectively develop Alzheimer’s disease (AD). Machine learning (ML) is suitable to improve early AD prediction. The etiology of AD is heterogeneous, which leads to high variability in disease patterns. Further variability originates from multicentric study designs, varying acquisition protocols, and errors in the preprocessing of magnetic resonance imaging (MRI) scans. The high variability makes the differentiation between signal and noise difficult and may lead to overfitting. This article examines whether an automatic and fair data valuation method based on Shapley values can identify the most informative subjects to improve ML classification. Methods: An ML workflow was developed and trained for a subset of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. The validation was executed for an independent ADNI test set and for the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) cohort. The workflow included volumetric MRI feature extraction, feature selection, sample selection using Data Shapley, random forest (RF), and eXtreme Gradient Boosting (XGBoost) for model training as well as Kernel SHapley Additive exPlanations (SHAP) values for model interpretation. Results: The RF models, which excluded 134 of the 467 training subjects based on their RF Data Shapley values, outperformed the base models that reached a mean accuracy of 62.64% by 5.76% (3.61 percentage points) for the independent ADNI test set. The XGBoost base models reached a mean accuracy of 60.00% for the AIBL data set. The exclusion of those 133 subjects with the smallest RF Data Shapley values could improve the classification accuracy by 2.98% (1.79 percentage points). The cutoff values were calculated using an independent validation set. Conclusion: The Data Shapley method was able to improve the mean accuracies for the test sets. The most informative subjects were associated with the number of ApolipoproteinE ε 4 (ApoE ε 4) alleles, cognitive test results, and volumetric MRI measurements.

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SHAP can equitably compare and rank the influence of diverse inputs-such as MRI-derived volumetric features, cognitive test scores, sociodemographic variables, and genetic markers like ApoE alleles-on a model's classification outcome.
“Its strength is particularly evident in multimodal diagnostic contexts, where it can equitably compare and rank the influence of diverse inputs—such as MRI-derived volumetric features, cognitive test scores, sociodemographic variables, and genetic markers like ApoE alleles—on a model’s classification outcome”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Bloch et al., 2021
The Results (Training of XGBoost classifiers; page unavailable) describe SHAP values that rank features by mean absolute value, including cognitive scores and MRI volumes. The Discussion also identifies ApoE ε4 alleles among important features. The Data sets section reports demographic variables, but the retrieved passages do not establish that SHAP compared their influence on classification outcomes; nor does the paper explicitly establish that the comparison is equitable. (LLM, conf=0.82)
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[17]D’Urso C EXPERIENCE: glitches in databases, how to ensure data quality by outlier detection techniques ACM J Data Inf Qual 2016 7 3 14
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Developing a machine learning workflow to identify noisy data in early Alzheimer’s disease detection based on Shapley values
Bloch, L., & Friedrich, C. M. (2021) · German Medical Science (German Research Foundation) · doi:10.3205/21gmds018
Self-citation
Machine Learning Workflow to Explain Black-Box Models for Early Alzheimer’s Disease Classification Evaluated for Multiple Datasets
Bloch, L., Friedrich, C. M., & Initiative, A. D. N. (2022) · SN Computer Science · doi:10.1007/s42979-022-01371-y · Cited by 33 · see citing works
Self-citation
XGBoost-SHAP-based interpretable diagnostic framework for alzheimer’s disease
Yi, F., Yang, H., Chen, D., Qin, Y., Han, H., Cui, J., Bai, W., Ma, Y., Zhang, R., & Yu, H. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02238-9 · Cited by 188 · see citing works
Optimizing Alzheimer's disease prediction through ensemble learning and feature interpretability with SHAP‐based feature analysis
Hossain, M. K., Ashraf, A., Islam, M. M., Sourav, S., & Shimul, M. M. H. (2025) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70162 · Cited by 8 · see citing works
Alzheimer's Prediction via eXplainable AI and SHAP-Driven Feature Attribution
Koli, L., & Reddy, A. S. (2025) · International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS) · doi:10.1109/icicnis66685.2025.11315815 · Cited by 3 · see citing works

Authors: Bloch, L., Friedrich, C. M., & Initiative, F. T. A. D. N.
Affiliations: Dortmund University of Applied Sciences and Arts (DE)
Corresponding author: Friedrich, Christoph M.
ORCID: Bloch, Louise
Friedrich, Christoph M.
Venue: Alzheimer's Research & Therapy
Publisher: Springer Science and Business Media LLC
Published: 2021-09-15
Volume: 13
Issue: 1
Pages: 155-155
ISSN: 1758-9193 (ISSN-L)
DOI: 10.1186/s13195-021-00879-4
PMC: PMC8444618
OpenAlex: W3133501805
Cited by: 68
References: 96
Citations by year: 2026: 9 · 2025: 15 · 2024: 19 · 2023: 11 · 2022: 14
FWCI: 8.07
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Alzheimer s Research & Therapy (publishedVersion, cc-by)
Research Square (acceptedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, other-oa)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
Funders: U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH-12, W81XWH1220012, AG024904)
Foundation for the National Institutes of Health (W81XWH-12-2-0012, U01AG024904, Grant U01 AG024904, AG024904)
Alzheimer's Association (U01 AG024904, W81XWH-12-2-0012)
Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
University of Southern California (W81XWH-12-2-0012, U01 AG024904)
F. Hoffmann-La Roche (W81XWH-12-2-0012, AG024904, U01 AG024904)
Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
Alzheimer's Disease Neuroimaging Initiative (W81XWH-12-2–0012, U01AG024904, AG024904, W81XWH-12-2-, Department of Defense award number W81XWH-12-2-0012)
BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
Novartis Pharmaceuticals Corporation (W81XWH-12-2-0012, U01 AG024904, AG024904)
Northern California Institute for Research and Education (U01 AG024904, W81XWH-12-2-0012)
Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
H. Lundbeck A/S (U01 AG024904, AG024904, W81XWH-12-2-0012)
IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
National Institutes of Health (3u01ag024904-10s2, W81XWH, W81XWH-12-2-0012, U01-AG024904, Grant U01 AG024904, AG024904)
Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
National Institute on Aging (U01-AG-024904)
National Institute of Biomedical Imaging and Bioengineering (W81XWH-12-2-0012, U01 AG024904, AG024904)
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords: Shapley value, sample selection, Alzheimer's disease, mild cognitive impairment, early disease prediction, model interpretability, APOE ε4, random forest, XGBoost, ADNI, AIBL
MeSH terms: Machine Learning, Data Analysis, Alzheimer Disease, Australia, Humans, Magnetic Resonance Imaging, Cognitive Dysfunction

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Data analysis with Shapley values for automatic subject selection in Alzheimer’s disease data sets using interpretable machine learning

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