*Result*: Comparative analysis of supervised and ensemble models with unsupervised exploration for alzheimer's disease prediction.
Title:
Comparative analysis of supervised and ensemble models with unsupervised exploration for alzheimer's disease prediction.
Authors:
Amr Y; Faculty of Media Engineering and Technology, German University in Cairo, New Cairo, Egypt., Gad W; Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt., Leiva V; School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile. victorleivasanchez@gmail.com.; Center for Interdisciplinary Research in Biomedicine, Biotechnology and Well-Being (CID3B), Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile. victorleivasanchez@gmail.com., Martin-Barreiro C; Facultad de Ciencias Naturales y Matemáticas, Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador. cmmartin@espol.edu.ec., Abdelkader T; Faculty of Computer Science and Engineering, Galala University, Galala, Egypt.
Source:
Scientific reports [Sci Rep] 2026 Feb 05; Vol. 16 (1). Date of Electronic Publication: 2026 Feb 05.
Publication Type:
Journal Article; Comparative Study
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms:
References:
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Davatzikos, C., Wang, Q., Shen, N. & Shen, L. Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging. J. Alzheimer’s Dis. 26, S307–S318 (2011).
Bustos, N. et al. Machine learning techniques as an efficient alternative diagnostic tool for COVID-19 cases. Signa Vitae 18, 23–33 (2022).
ElNaqa, A., Patel, M. & Al-Azzawi, R. Explainable machine learning models for Alzheimer’s diagnosis using high-dimensional neuroimaging data. J. Biomed. Inform. 150, 104225 (2024).
Li, M., Liu, H., Li, Y., Wang, Z., Yuan, Y. & Dai, H. Intelligent diagnosis of Alzheimer’s disease based on machine learning. Proceedings of the 4th International Symposium on Artificial Intelligence for Medicine Science, Chengdu, China, pp. 456–462. Available at https://doi.org/10.1145/3644116.3644192 (2024).
Zhou, Z. H. Ensemble methods in machine learning. Encycl. Mach. Learn. 5, 312–320 (2012).
Chatterjee, S. & Byun, Y. C. Voting ensemble approach for enhancing Alzheimer’s disease classification. Sensors 22, 7661 (2022). (PMID: 10.3390/s22197661362367579571155)
Mahajan, A., Gupta, R. & Sharma, P. Ensemble learning for disease prediction: A review. Healthcare 11, 1808 (2023). (PMID: 10.3390/healthcare111218083737292510298658)
Praveen, S. P. et al. Enhanced predictive modeling for Alzheimer’s disease: Integrating cluster-based boosting and gradient techniques with optimized feature selection. J. Theor. Appl. Inf. Technol. 103, 624–631 (2025).
Baglat, P. et al. Early detection of Alzheimer’s disease in structural and functional MRI using segmentation and hybrid classifiers. Front. Med. 11, 1376545 (2024).
Shaffi, N., Viswan, V. & Mahmud, M. Ensemble of vision transformer architectures for efficient Alzheimer’s disease classification. Brain Inform. 11, 25 (2024). (PMID: 10.1186/s40708-024-00238-73936312211450128)
Zhang, H., Dong, B., Han, J. & Huang, L. Interpretable machine learning models for survival prediction in prostate cancer bone metastases. Sci. Rep. 15, 24150 (2025). (PMID: 10.1038/s41598-025-09691-84061946312230155)
Amin, S., Hussain, A. & Kim, B. Deep learning based active learning technique for Alzheimer’s disease detection. Expert. Syst. Appl. 228, 120391 (2023). (PMID: 10.1016/j.eswa.2023.120391)
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Antor, M. B. et al. A comparative analysis of machine learning algorithms to predict Alzheimer’s disease. J. Healthc. Eng. 2021, 9917919 (2021).
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He, H. & Garcia, E. A. Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21, 1263–1284 (2009). (PMID: 10.1109/TKDE.2008.239)
Talwar, A., Lopez-Olivo, M. A., Huang, Y., Ying, L. & Aparasu, R. R. Performance of advanced machine learning algorithms over logistic regression in predicting hospital readmissions: A meta-analysis. Explor. Res. Clin. Soc. Pharm. 11, 100317 (2023). (PMID: 3766269710474076)
Zhu, B., Jing, X., Qiu, L. & Li, R. An imbalanced data classification method based on hybrid resampling and fine cost-sensitive support vector machine. Comput., Mater. Contin. 79, 3977–3999 (2024).
Quinlan, J. R. Induction of decision trees. Mach. Learn. 1, 81–106 (1986). (PMID: 10.1023/A:1022643204877)
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001). (PMID: 10.1023/A:1010933404324)
Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997). (PMID: 10.1006/jcss.1997.1504)
Chen, T, & Guestrin, C. XGBoost: A scalable tree boosting system. Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, pp. 785–794. Available at https://doi.org/10.1145/2939672.2939785 (2016).
Rokach, L. Ensemble-based classifiers. Artif. Intell. Rev. 33, 1–39 (2010). (PMID: 10.1007/s10462-009-9124-7)
Stern, Y. What is cognitive reserve? Theory and research application of the reserve concept. J. Int. Neuropsychol. Soc. 8, 448–460 (2002). (PMID: 10.1017/S135561770281324811939702)
Alkadya, W., ElBahnasy, K., Leiva, V. & Gad, W. Classifying COVID-19 based on amino acids encoding with machine learning algorithms. Chemom. Intell. Lab. Syst. 224, 104535 (2022). (PMID: 10.1016/j.chemolab.2022.104535)
Ma, L., Zhang, Y., Leiva, V., Liu, S. & Ma, T. A new clustering algorithm based on a radar scanning strategy with applications to machine learning data. Expert. Syst. Appl. 191, 116143 (2022). (PMID: 10.1016/j.eswa.2021.116143)
Sardar, I., Akbar, M., Leiva, V., Alsanad, A. & Mishra, P. Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: Methodology, evaluation, case study in SAARC countries. Stoch. Environ. Res. Risk Assess. 37, 345–359 (2023). (PMID: 10.1007/s00477-022-02307-x36217358)
Ospina, R., Gondim, J. A. M., Leiva, V. & Castro, C. An overview of forecast analysis with ARIMA models during the COVID-19 pandemic: Methodology and case study in Brazil. Mathematics 11, 3069 (2023). (PMID: 10.3390/math11143069)
Bibi, A. Statistical inference for first-order periodic autoregressive conditional heteroscedasticity models. Chil. J. Stat. 15, 169–191 (2024).
Sozen, C. Short-horizon volatility spike forecasting via integrated functional and topological representations. Chil. J. Stat. 16, 178–216 (2025).
Nor, A. K. M., Pedapati, S. R., Muhammad, M. & Leiva, V. Overview of explainable artificial intelligence for prognostic and health management of industrial assets based on preferred reporting items for systematic reviews and meta-analyses. Sensor 21, 8020 (2021). (PMID: 10.3390/s21238020348840248659640)
Nor, A. K. M., Pedapati, S. R. & Muhammad, M. Abnormality detection and failure prediction using explainable Bayesian deep learning: Methodology and case study with industrial data. Mathematics 10, 554 (2022). (PMID: 10.3390/math10040554)
Ramirez-Figueroa, J. A., Cabezas, X., Martin-Casado, A. & Galindo-Villardón, M. P. A new algorithm for computing disjoint orthogonal components in the parallel factor analysis model with simulations and applications to real-world data. Mathematics 9, 2058 (2021). (PMID: 10.3390/math9172058)
Suárez, C. A., Castro, M., Leon, M., Martin-Barreiro, C. & Liut, M. Improving SVM performance through data reduction and misclassification analysis with linear programming. Complex Intell. Syst. 11, 356 (2025). (PMID: 10.1007/s40747-025-01989-4)
Kotsiantis, S. B. Supervised machine learning: A review of classification techniques. Informatica 31, 249–268 (2007).
Jordan, M. I. & Mitchell, T. M. Machine learning: Trends, perspectives, prospects. Science 349, 255–260 (2015). (PMID: 10.1126/science.aaa841526185243)
Li, Y. et al. Developing angiogenesis-related prognostic biomarkers and therapeutic strategies in bladder cancer using deep learning and machine learning. Sci. Rep. 15, 25534 (2025). (PMID: 10.1038/s41598-025-08945-94066500812263826)
Leiva, V. et al. A real-time intelligent system based on machine-learning methods for improving communication in sign language. IEEE Access 13, 22055–22073 (2025). (PMID: 10.1109/ACCESS.2025.3529025)
Davatzikos, C., Wang, Q., Shen, N. & Shen, L. Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging. J. Alzheimer’s Dis. 26, S307–S318 (2011).
Bustos, N. et al. Machine learning techniques as an efficient alternative diagnostic tool for COVID-19 cases. Signa Vitae 18, 23–33 (2022).
ElNaqa, A., Patel, M. & Al-Azzawi, R. Explainable machine learning models for Alzheimer’s diagnosis using high-dimensional neuroimaging data. J. Biomed. Inform. 150, 104225 (2024).
Li, M., Liu, H., Li, Y., Wang, Z., Yuan, Y. & Dai, H. Intelligent diagnosis of Alzheimer’s disease based on machine learning. Proceedings of the 4th International Symposium on Artificial Intelligence for Medicine Science, Chengdu, China, pp. 456–462. Available at https://doi.org/10.1145/3644116.3644192 (2024).
Zhou, Z. H. Ensemble methods in machine learning. Encycl. Mach. Learn. 5, 312–320 (2012).
Chatterjee, S. & Byun, Y. C. Voting ensemble approach for enhancing Alzheimer’s disease classification. Sensors 22, 7661 (2022). (PMID: 10.3390/s22197661362367579571155)
Mahajan, A., Gupta, R. & Sharma, P. Ensemble learning for disease prediction: A review. Healthcare 11, 1808 (2023). (PMID: 10.3390/healthcare111218083737292510298658)
Praveen, S. P. et al. Enhanced predictive modeling for Alzheimer’s disease: Integrating cluster-based boosting and gradient techniques with optimized feature selection. J. Theor. Appl. Inf. Technol. 103, 624–631 (2025).
Baglat, P. et al. Early detection of Alzheimer’s disease in structural and functional MRI using segmentation and hybrid classifiers. Front. Med. 11, 1376545 (2024).
Shaffi, N., Viswan, V. & Mahmud, M. Ensemble of vision transformer architectures for efficient Alzheimer’s disease classification. Brain Inform. 11, 25 (2024). (PMID: 10.1186/s40708-024-00238-73936312211450128)
Zhang, H., Dong, B., Han, J. & Huang, L. Interpretable machine learning models for survival prediction in prostate cancer bone metastases. Sci. Rep. 15, 24150 (2025). (PMID: 10.1038/s41598-025-09691-84061946312230155)
Amin, S., Hussain, A. & Kim, B. Deep learning based active learning technique for Alzheimer’s disease detection. Expert. Syst. Appl. 228, 120391 (2023). (PMID: 10.1016/j.eswa.2023.120391)
Zhou, Q., Wang, J., Yu, X., Wang, S. & Zhang, Y. A survey of deep learning for Alzheimer’s Disease. Mathematics 10, 1555 (2023).
Mehmood, A., Abugabah, A., AlZubi, A. A. & Sanzogni, L. Early diagnosis of Alzheimer’s disease based on convolutional neural networks. Comput. Syst. Sci. Eng. 43, 305–315 (2023). (PMID: 10.32604/csse.2022.018520)
Antor, M. B. et al. A comparative analysis of machine learning algorithms to predict Alzheimer’s disease. J. Healthc. Eng. 2021, 9917919 (2021).
Marcus, D. S. et al. Open access series of imaging studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, demented older adults. J. Cogn. Neurosci. 19, 1498–1507 (2007). (PMID: 10.1162/jocn.2007.19.9.149817714011)
Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
He, H. & Garcia, E. A. Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21, 1263–1284 (2009). (PMID: 10.1109/TKDE.2008.239)
Talwar, A., Lopez-Olivo, M. A., Huang, Y., Ying, L. & Aparasu, R. R. Performance of advanced machine learning algorithms over logistic regression in predicting hospital readmissions: A meta-analysis. Explor. Res. Clin. Soc. Pharm. 11, 100317 (2023). (PMID: 3766269710474076)
Zhu, B., Jing, X., Qiu, L. & Li, R. An imbalanced data classification method based on hybrid resampling and fine cost-sensitive support vector machine. Comput., Mater. Contin. 79, 3977–3999 (2024).
Quinlan, J. R. Induction of decision trees. Mach. Learn. 1, 81–106 (1986). (PMID: 10.1023/A:1022643204877)
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001). (PMID: 10.1023/A:1010933404324)
Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997). (PMID: 10.1006/jcss.1997.1504)
Chen, T, & Guestrin, C. XGBoost: A scalable tree boosting system. Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, pp. 785–794. Available at https://doi.org/10.1145/2939672.2939785 (2016).
Rokach, L. Ensemble-based classifiers. Artif. Intell. Rev. 33, 1–39 (2010). (PMID: 10.1007/s10462-009-9124-7)
Stern, Y. What is cognitive reserve? Theory and research application of the reserve concept. J. Int. Neuropsychol. Soc. 8, 448–460 (2002). (PMID: 10.1017/S135561770281324811939702)
Alkadya, W., ElBahnasy, K., Leiva, V. & Gad, W. Classifying COVID-19 based on amino acids encoding with machine learning algorithms. Chemom. Intell. Lab. Syst. 224, 104535 (2022). (PMID: 10.1016/j.chemolab.2022.104535)
Ma, L., Zhang, Y., Leiva, V., Liu, S. & Ma, T. A new clustering algorithm based on a radar scanning strategy with applications to machine learning data. Expert. Syst. Appl. 191, 116143 (2022). (PMID: 10.1016/j.eswa.2021.116143)
Sardar, I., Akbar, M., Leiva, V., Alsanad, A. & Mishra, P. Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: Methodology, evaluation, case study in SAARC countries. Stoch. Environ. Res. Risk Assess. 37, 345–359 (2023). (PMID: 10.1007/s00477-022-02307-x36217358)
Ospina, R., Gondim, J. A. M., Leiva, V. & Castro, C. An overview of forecast analysis with ARIMA models during the COVID-19 pandemic: Methodology and case study in Brazil. Mathematics 11, 3069 (2023). (PMID: 10.3390/math11143069)
Bibi, A. Statistical inference for first-order periodic autoregressive conditional heteroscedasticity models. Chil. J. Stat. 15, 169–191 (2024).
Sozen, C. Short-horizon volatility spike forecasting via integrated functional and topological representations. Chil. J. Stat. 16, 178–216 (2025).
Nor, A. K. M., Pedapati, S. R., Muhammad, M. & Leiva, V. Overview of explainable artificial intelligence for prognostic and health management of industrial assets based on preferred reporting items for systematic reviews and meta-analyses. Sensor 21, 8020 (2021). (PMID: 10.3390/s21238020348840248659640)
Nor, A. K. M., Pedapati, S. R. & Muhammad, M. Abnormality detection and failure prediction using explainable Bayesian deep learning: Methodology and case study with industrial data. Mathematics 10, 554 (2022). (PMID: 10.3390/math10040554)
Ramirez-Figueroa, J. A., Cabezas, X., Martin-Casado, A. & Galindo-Villardón, M. P. A new algorithm for computing disjoint orthogonal components in the parallel factor analysis model with simulations and applications to real-world data. Mathematics 9, 2058 (2021). (PMID: 10.3390/math9172058)
Suárez, C. A., Castro, M., Leon, M., Martin-Barreiro, C. & Liut, M. Improving SVM performance through data reduction and misclassification analysis with linear programming. Complex Intell. Syst. 11, 356 (2025). (PMID: 10.1007/s40747-025-01989-4)
Contributed Indexing:
Keywords: Clinical profiling; Cluster analysis; Data analysis models; Dementia prediction; Dimensionality reduction; Pattern discovery; Statistical analysis
Entry Date(s):
Date Created: 20260204 Date Completed: 20260220 Latest Revision: 20260223
Update Code:
20260223
PubMed Central ID:
PMC12923517
DOI:
10.1038/s41598-026-37122-9
PMID:
41639293
Database:
MEDLINE
*Further Information*
*Alzheimer's disease is a progressive neurodegenerative disorder characterized by memory loss and cognitive decline, with no known cure. Early detection of dementia, a primary manifestation of Alzheimer's disease, is critical to enable timely intervention and treatment planning. This study introduces ensemble learning models for predicting Alzheimer's disease and presents a comparative analysis between traditional machine learning and advanced ensemble models. The evaluation is conducted using the "Open Access Series of Imaging Studies" 2 (OASIS-2) dataset. Traditional models, including logistic regression, decision tree, support vector machine, and random forest, are benchmarked against ensemble models such as adaptive boosting, extreme gradient boosting, and a hyperparameter-tuned majority voting ensemble models. Performance is assessed using accuracy, precision, and the area under the receiver operating characteristic curve. Results show that ensemble models, particularly the optimized majority voting classifier, consistently outperform traditional methods. To complement the supervised comparison, exploratory unsupervised methods were applied using multiple correspondence analysis and k-means clustering to uncover latent structures in the dataset. By categorizing all variables, these unsupervised methods highlight patterns of clinical and demographic similarity. Unlike prior studies that focus solely on predictive accuracy, this work integrates supervised classification, ensemble learning, and unsupervised exploratory analysis within a unified framework. This combined approach enables both robust performance comparison and deeper insights into latent data structures relevant to Alzheimer's disease. All computational experiments were conducted using the Python programming language.
(© 2026. The Author(s).)*
*Declarations. Competing interests: The authors declare that they have no competing interests. Use of AI tools: The authors declare they have not used artificial intelligence (AI) tools in the creation of this article.*