Ensemble-Based Cognitive Biomarker Analysis for Predicting Early-Stage Alzheimer’s disease
Keywords:
Adaptive voting, Alzheimer’s disease (AD), cognitive features, machine learning (ML), Neighborhood Component Analysis and Correlation based Filtration (NCA-F)Abstract
Opportune recognizable proof of "Alzheimer's disease” is critical for ideal guideline and organization. Early recognizable proof of the condition works with brief activity and upgrades results for those impacted. This study coordinates mental characteristics with a troupe "machine learning" approach for the recognizable proof of "Alzheimer's disease”. Groups use the upsides of different "machine learning" models to work on conjecture precision. The exploration involves the assortment and readiness of a dataset including mental evaluations from people determined to have and without "Alzheimer's disease". This data comprises the reason for the "machine learning "model. Highlight choice techniques are used to decide the most relevant mental qualities for the identification of "Alzheimer's disease". This stage is fundamental in underscoring the mental components that are generally reminiscent of the condition. The examination presents an imaginative element determination method known as "Neighborhood Component Analysis and Correlation-based Filtration (NCA-F)". This procedure plans to extricate basic
mental qualities from the dataset, thus working on the data's significance for "Alzheimer's disease" discovery. The proposed technique extraordinarily works on the accuracy of beginning phase "Alzheimer's disease" recognition. This improvement is a urgent consequence of the undertaking, connoting its forthcoming viability in the early conclusion and mediation of "Alzheimer's disease". The undertaking upgrades its capacities by coordinating high level machine learning models, like "Convolutional Neural Networks (CNN), CNN joined with " Long Short-Term Memory (LSTM)", and a profoundly viable Stacking Classifier, accomplishing an exceptional 100 accuracy in the better early recognition of "Alzheimer's Disease
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Sekhar, J. C., et al. "Deep generative adversarial networks with marine predators algorithm for classification of Alzheimer’s disease using electroencephalogram." Journal of King Saud University-Computer and Information Sciences 35.10 (2023): 101848.
Alzheimer’s Association. (2016). Alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 12(4), 459–509.
Tarawneh, R. and Holtzman, D. M. (2012). The clinical problem of symptomatic Alzheimer’s disease and mild cognitive impairment. Cold Spring Harbor Perspectives in Medicine, 2(5), a006148.
World Health Organization. Dementia: Fact sheet, 2017. Accessed: Jan. 4, 2021. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/ dementia
Ahmadi-Abhari, S. et al (2017). Temporal trend in dementia incidence since 2002 and projections for prevalence in England and Wales to 2040: Modelling study. BMJ, 358, j2856.
Australian Institute of Health. (2012). Australia’s Health 2012: The Thirteenth Biennial Health Report of the Australian Institute of Health and Welfare. Sydney, Australia: AIHW.
Brookmeyer, R., Abdalla, N., Kawas, C. H., and Corrada, M. M. (2018). Forecasting the prevalence of preclinical and clinical Alzheimer’s disease in the United States. Alzheimer’s & Dementia, 14(2), 121–129.
Matthews, F. E. et al (2016). A two decade dementia incidence comparison from the cognitive function and ageing studies I and II. Nature Communications, 7(1), 1–8.
Sullivan, K. J. et al (2019). Declining incident dementia rates across four population-based birth cohorts. The Journals of Gerontology: Series A, 74(9), 1439–1445.
Gangula, R., Manjula, A., Bhukya, R., Saturi, R., Gayatri, N., & Rajesh, R. (2024). Innovative Approaches for Early Alzheimer’s Disease Detection through Novel Analysis of Brain MRI Images. International Journal of Maritime Engineering, 1(1), 761-772.
Gurevich, P., Stuke, H., Kastrup, A., Stuke, H., and Hildebrandt, H. (2017). Neuropsychological testing and machine learning distinguish Alzheimer’s disease from other causes for cognitive impairment. Frontiers in Aging Neuroscience, 9, 114.
Martinez-Murcia, F. J., Ortiz, A., Gorriz, J. M., Ramirez, J., and Castillo-Barnes, D. (Jan. 2020). Studying the manifold structure of Alzheimer’s disease: A deep learning approach using convolutional autoencoders. IEEE Journal of Biomedical and Health Informatics, 24(1), 17–26.
Shen, T. et al (2019). Predicting Alzheimer disease from mild cognitive impairment with a deep belief network based on 18F-FDG-PET Images. Molecular Imaging, 18, 1536012119877285.
Feng, C. et al (2019). Deep learning framework for Alzheimer’s disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access, 7, 63605–63618.
Battista, P., Salvatore, C., and Castiglioni, I. (2017). Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: A machine learning study. Behavioural Neurology, 2017, 1850909.
Youn, Y. C. et al (2018). Detection of cognitive impairment using a machinelearning algorithm. Neuropsychiatric Disease and Treatment, 14, 2939.
Kruthika, K. R., Maheshappa, H. D., and Alzheimer’s Disease Neuroimaging Initiative. (2019). Multistage classifier-based approach for Alzheimer’s disease prediction and retrieval. Informatics in Medicine Unlocked, 14, 34–42.
Veeramuthu, A., Meenakshi, S., and Manjusha, P. S. (2014). A new approach for Alzheimer’s disease diagnosis by using association rule over pet images. International Journal of Computer Applications, 91(9), 9–14.
Kang, M. J. et al (2019). Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data. BMC Medical Informatics and Decision Making, 19(1), 1–9.
Zhu, F. et al (2020). Machine learning for the preliminary diagnosis of dementia. Scientific Programming, 2020, 5629090.
You, Y., Ahmed, B., Barr, P., Ballard, K., and Valenzuela, M. (2012). Predicting dementia risk using paralinguistic and memory test features with machine learning models. in Proceeding IEEE Healthcare Innovations and Point of Care Technologies, pp. 56–59.
Grassi, M. et al (2019). A novel ensemble-based machine learning algorithm to predict the conversion from mild cognitive impairment to Alzheimer’s disease using socio-demographic characteristics, clinical information, and neuropsychological measures. Frontiers in Neurology, 10, 756.
Schmand, B., Walstra, G., Lindeboom, J., Teunisse, S., and Jonker, C. (2000). Early detection of Alzheimer’s disease using the Cambridge Cognitive Examination (CAMCOG). Psychological Medicine, 30(3), 619–627.
Chapman, R. M. et al (2010). Diagnosis of Alzheimer’s disease using neuropsychological testing improved by multivariate analyses. Journal of Clinical and Experimental Neuropsychology, 32(8), 793–808.
Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L. A., Eds. (2008). Feature extraction: Foundations and Applications, vol. 207. Berlin, Germany: SpringerVerlag.
Fisher, R. A. (1992). Statistical methods for research workers. in Breakthroughs in Statistics. Berlin, Germany: Springer-Verlag, pp. 66–70.
Zhou, T., Thung, K. H., Zhu, X., and Shen, D. (2019). Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Human Brain Mapping, 40(3), 1001–1016.
Yang, W., Wang, K., and Zuo, W. (2012). Neighborhood component feature selection for high-dimensional data. Journal Computational, 7(1), 161–168.
Gill, S. et al. (2020). Using machine learning to predict dementia from neuropsychiatric symptom and neuroimaging data. Journal of Alzheimer’s Disease, 75(1), 277–288.
Ford, E. et al (2019). Identifying undetected dementia in UK primary care patients: A retrospective case-control study comparing machine-learning and standard epidemiological approaches. BMC Medical Informatics and Decision Making, 19(1), 1–9.
Weakley, A., Williams, J. A., Schmitter-Edgecombe, M., and Cook, D. J. (2015). Neuropsychological test selection for cognitive impairment classification: A machine learning approach. Journal of Clinical and Experimental Neuropsychology, 37(9), 899–916.
Weiner, M. W. et al. (2010). The Alzheimer’s disease neuroimaging initiative: Progress report and future plans. Alzheimer’s & Dementia, 6(3), 202–211.
Petersen, R. and Weiner, M. W. (2014). Alzheimer’s disease neuroimaging initiative 2 (ADNI2) protocol (ADC-039). [Online]. Available: https://adni.loni. usc.edu/wp-content/themes/freshnews-dev-v2/documents/clinical/ADNI- 2_Protocol.pdf.
Weiner, M. W. et al. (2017). The Alzheimer’s disease neuroimaging initiative 3: Continued innovation for clinical trial improvement. Alzheimer’s & Dementia, 13 (5), 561–571.
Van Buuren, S. and Groothuis-Oudshoorn, K. (2011) mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45, 1–67.
Zang, I. (1980). A smoothing-out technique for min–max optimization. Mathematical Programming, 19(1), 61–77.
Yang, W., Wang, K., and Zuo, W. (2012). Fast neighborhood component analysis. Neurocomputing, 83, 31–37.
Ketkar, N. (2017). Stochastic gradient descent. in Deep Learning With Python. Berkeley, CA, USA: Apress, pp. 113–132.
Kontorovich, A. and Weiss, R. (2015). A Bayes consistent 1-NN classifier. in Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, pp. 480–488.
Zhang, C., and Ma, Y., Eds. (2012). Ensemble Machine Learning: Methods and Applications. Berlin, Germany: Springer-Verlag.
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E. (1991). Adaptive mixtures of local experts. Neural Computation, 3(1), 79–87.
Woods, K., Kegelmeyer, W. P., and Bowyer, K. (Apr. 1997). Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4), 405–410.
Alpaydin, E. and Jordan, M. I. (May 1996). Local linear perceptrons for classification. IEEE Transactions on Neural Networks, 7(3), 788–794.
Giacinto, G. and Roli, F. (2001). An approach to the automatic design of multiple classifier systems. Pattern Recognition Letters, 22(1), 25–33.
Inoue, H. (2019). Adaptive ensemble prediction for deep neural networks based on confidence level. in Proceeding 22nd International Conference on Artificial Intelligence and Statistics, pp. 1284–1293.
Wang, C., Deng, C., Yu, Z., Hui, D., Gong, X., and Luo, R. (2021). Adaptive ensemble of classifiers with regularization for imbalanced data classification. Information Fusion, 69, 81–102.
Irfan, M., Shahrestani, S., and Elkhodr, M. (2021). Early detection of the Alzheimer’s disease: A novel cognitive feature selection approach using machine learning. in Proceeding International Conference on Information, Communication Cybersecurity, pp. 383–392.
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