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Current Alzheimer Research

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Research Article

Machine-Based Learning Shifting to Prediction Model of Deteriorative MCI Due to Alzheimer’s Disease - A Two-Year Follow-Up Investigation

Author(s): Xiaohui Zhao, Haijing Sui, Chengong Yan, Min Zhang, Haihan Song, Xueyuan Liu and Juan Yang*

Volume 19, Issue 10, 2022

Published on: 03 November, 2022

Page: [708 - 715] Pages: 8

DOI: 10.2174/1567205020666221019122049

Price: $65

Abstract

Objective: The aim of the present work was to investigate the features of the elderly population aged ≥65 yrs and with deteriorative mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) to establish a prediction model.

Methods: A total of 105 patients aged ≥65 yrs and with MCI were followed up, with a collection of 357 features, which were derived from the demographic characteristics, hematological indicators (serum Aβ1-40, Aβ1-42, P-tau and MCP-1 levels, APOE gene), and multimodal brain Magnetic Resonance Imaging (MRI) imaging indicators of 116 brain regions (ADC, FA and CBF values). Cognitive function was followed up for 2 yrs. Based on the Python platform Anaconda, 105 patients were randomly divided into a training set (70%) and a test set (30%) by analyzing all features through a random forest algorithm, and a prediction model was established for the form of rapidly deteriorating MCI.

Results: Of the 105 patients enrolled, 41 deteriorated, and 64 did not come within 2 yrs. Model 1 was established based on demographic characteristics, hematological indicators and multi-modal MRI image features, the accuracy of the training set being 100%, the accuracy of the test set 64%, sensitivity 50%, specificity 67%, and AUC 0.72. Model 2 was based on the first five features (APOE4 gene, FA value of left fusiform gyrus, FA value of left inferior temporal gyrus, FA value of left parahippocampal gyrus, ADC value of right calcarine fissure as surrounding cortex), the accuracy of the training set being 100%, the accuracy of the test set 85%, sensitivity 91%, specificity 80% and AUC 0.96. Model 3 was based on the first four features of Model 1, the accuracy of the training set is 100%, the accuracy of the test set 97%, sensitivity100%, specificity 95% and AUC 0.99. Model 4 was based on the first three characteristics of Model 1, the accuracy of the training set being 100%, the accuracy of the test set 94%, sensitivity 92%, specificity 94% and AUC 0.96. Model 5 was based on the hematological characteristics, the accuracy of the training set is 100%, the accuracy of the test set 91%, sensitivity 100%, specificity 88% and AUC 0.97. The models based on the demographic characteristics, imaging characteristics FA, CBF and ADC values had lower sensitivity and specificity.

Conclusion: Model 3, which has four important predictive characteristics, can predict the rapidly deteriorating MCI due to AD in the community.

Keywords: Machine learning, random forest, Alzheimer’s disease, mild cognitive impairment, prediction model, magnetic resonance imaging.

[1]
Aisen PS, Cummings J, Jack CR Jr, et al. On the path to 2025: Understanding the Alzheimer’s disease continuum. Alzheimers Res Ther 2017; 9(1): 60.
[http://dx.doi.org/10.1186/s13195-017-0283-5] [PMID: 28793924]
[2]
Arora P, Boyne D, Slater JJ, Gupta A, Brenner DR, Druzdzel MJ. Bayesian networks for risk prediction using real-world data: A tool for precision medicine. Value Health 2019; 22(4): 439-45.
[http://dx.doi.org/10.1016/j.jval.2019.01.006] [PMID: 30975395]
[3]
Lopez OL, Kuller LH. Epidemiology of aging and associated cognitive disorders: Prevalence and incidence of Alzheimer’s disease and other dementias. Handb Clin Neurol 2019; 167: 139-48.
[http://dx.doi.org/10.1016/B978-0-12-804766-8.00009-1] [PMID: 31753130]
[4]
Jia J, Wei C, Chen S, et al. The cost of Alzheimer’s disease in China and re‐estimation of costs worldwide. Alzheimers Dement 2018; 14(4): 483-91.
[http://dx.doi.org/10.1016/j.jalz.2017.12.006] [PMID: 29433981]
[5]
Monier M, El-Mekabaty A, Abdel-Latif D, Doğru MB, Elattar KM. Heterocyclic steroids: Efficient routes for annulation of pentacyclic steroidal pyrimidines. Steroids 2020; 154: 108548.
[http://dx.doi.org/10.1016/j.steroids.2019.108548] [PMID: 31805293]
[6]
Jack CR Jr, Bennett DA, Blennow K, et al. NIA‐AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018; 14(4): 535-62.
[http://dx.doi.org/10.1016/j.jalz.2018.02.018] [PMID: 29653606]
[7]
Petersen RC, Wiste HJ, Weigand SD, et al. NIA‐AA Alzheimer’s disease framework: Clinical characterization of stages. Ann Neurol 2021; 89(6): 1145-56.
[http://dx.doi.org/10.1002/ana.26071] [PMID: 33772866]
[8]
Carmichael O, Newton R Jr. Brain MRI findings related to Alzheimer’s disease in older African American adults. Prog Mol Biol Transl Sci 2019; 165: 3-23.
[http://dx.doi.org/10.1016/bs.pmbts.2019.04.002] [PMID: 31481168]
[9]
Zhou Y, Song Z, Han X, Li H, Tang X. Prediction of Alzheimer’s disease progression based on magnetic resonance imaging. ACS Chem Neurosci 2021; 12(22): 4209-23.
[http://dx.doi.org/10.1021/acschemneuro.1c00472] [PMID: 34723463]
[10]
Heo J, Yoon JG, Park H, Kim YD, Nam HS, Heo JH. Machine learning–based model for prediction of outcomes in acute stroke. Stroke 2019; 50(5): 1263-5.
[http://dx.doi.org/10.1161/STROKEAHA.118.024293] [PMID: 30890116]
[11]
Iwendi C, Bashir AK, Peshkar A, et al. COVID-19 patient health prediction using boosted random forest algorithm. Front Public Health 2020; 8: 357.
[http://dx.doi.org/10.3389/fpubh.2020.00357] [PMID: 32719767]
[12]
Asadi S, Roshan S, Kattan MW. Random forest swarm optimization-based for heart diseases diagnosis. J Biomed Inform 2021; 115: 103690.
[http://dx.doi.org/10.1016/j.jbi.2021.103690] [PMID: 33540075]
[13]
Hu WS, Hsieh MH, Lin CL. A novel atrial fibrillation prediction model for Chinese subjects: A nationwide cohort investigation of 682 237 study participants with random forest model. Europace 2019; 21(9): 1307-12.
[http://dx.doi.org/10.1093/europace/euz036] [PMID: 31067312]
[14]
Zhou JY, Song LW, Yuan R, Lu XP, Wang GQ. Prediction of hepatic inflammation in chronic hepatitis B patients with a random forest-backward feature elimination algorithm. World J Gastroenterol 2021; 27(21): 2910-20.
[http://dx.doi.org/10.3748/wjg.v27.i21.2910] [PMID: 34135561]
[15]
Reisberg B, Ferris SH, de Leon MJ, Crook T. The Global Deterioration Scale for assessment of primary degenerative dementia. Am J Psychiatry 1982; 139(9): 1136-9.
[http://dx.doi.org/10.1176/ajp.139.9.1136] [PMID: 7114305]
[16]
Corbi A, Burgos D. Connection between sleeping patterns and cognitive deterioration in women with Alzheimer’s disease. Sleep Breath 2022; 26(1): 361-71.
[http://dx.doi.org/10.1007/s11325-021-02327-x ] [PMID: 33792886]
[17]
Avants B, Epstein C, Grossman M, Gee J. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 2008; 12(1): 26-41.
[http://dx.doi.org/10.1016/j.media.2007.06.004] [PMID: 17659998]
[18]
Rolls ET, Joliot M, Tzourio-Mazoyer N. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. Neuroimage 2015; 122: 1-5.
[http://dx.doi.org/10.1016/j.neuroimage.2015.07.075] [PMID: 26241684]
[19]
Tang X, Liu J. Comparing different algorithms for the course of Alzheimer’s disease using machine learning. Ann Palliat Med 2021; 10(9): 9715-24.
[http://dx.doi.org/10.21037/apm-21-2013] [PMID: 34628897]
[20]
Song M, Jung H, Lee S, Kim D, Ahn M. Diagnostic classification and biomarker identification of Alzheimer’s disease with random forest algorithm. Brain Sci 2021; 11(4): 453.
[http://dx.doi.org/10.3390/brainsci11040453] [PMID: 33918453]
[21]
Chang CH, Lin CH, Liu CY, et al. Plasma d -glutamate levels for detecting mild cognitive impairment and Alzheimer’s disease: Machine learning approaches. J Psychopharmacol 2021; 35(3): 265-72.
[http://dx.doi.org/10.1177/0269881120972331] [PMID: 33586518]
[22]
Elias-Sonnenschein LS, Viechtbauer W, Ramakers IHGB, Verhey FRJ, Visser PJ. Predictive value of APOE-4 allele for progression from MCI to AD-type dementia: A meta-analysis. J Neurol Neurosurg Psychiatry 2011; 82(10): 1149-56.
[http://dx.doi.org/10.1136/jnnp.2010.231555] [PMID: 21493755]
[23]
Valero S, Marquié M, De Rojas I, et al. Interaction of neuropsychiatric symptoms with APOE ε4 and conversion to dementia in MCI patients in a Memory Clinic. Sci Rep 2020; 10(1): 20058.
[http://dx.doi.org/10.1038/s41598-020-77023-z] [PMID: 33208795]
[24]
Shi JY, Wang P, Wang BH, Xu Y, Chen X, Li HJ. Brain homotopic connectivity in mild cognitive impairment APOE-ε4 carriers. Neuroscience 2020; 436: 74-81.
[http://dx.doi.org/10.1016/j.neuroscience.2020.04.011] [PMID: 32304722]
[25]
Tennant VR, Harrison TM, Adams JN, La Joie R, Winer JR, Jagust WJ. Fusiform gyrus phospho‐tau is associated with failure of proper name retrieval in aging. Ann Neurol 2021; 90(6): 988-93.
[http://dx.doi.org/10.1002/ana.26237] [PMID: 34590340]
[26]
Ma D, Fetahu IS, Wang M, et al. The fusiform gyrus exhibits an epigenetic signature for Alzheimer’s disease. Clin Epigenetics 2020; 12(1): 129.
[http://dx.doi.org/10.1186/s13148-020-00916-3] [PMID: 32854783]
[27]
Kim D, Lee JY, Jeong BC, et al. Overconnectivity of the right Heschl’s and inferior temporal gyrus correlates with symptom severity in preschoolers with autism spectrum disorder. Autism Res 2021; 14(11): 2314-29.
[http://dx.doi.org/10.1002/aur.2609] [PMID: 34529363]
[28]
Zhu L, Wang Z, Du Z, et al. Impaired parahippocampal gyrus–orbitofrontal cortex circuit associated with visuospatial memory deficit as a potential biomarker and interventional approach for Alzheimer disease. Neurosci Bull 2020; 36(8): 831-44.
[http://dx.doi.org/10.1007/s12264-020-00498-3] [PMID: 32350798]
[29]
Lin YH, Dhanaraj V, Mackenzie AE, et al. Anatomy and white matter connections of the parahippocampal gyrus. World Neurosurg 2021; 148: e218-26.
[http://dx.doi.org/10.1016/j.wneu.2020.12.136] [PMID: 33412321]

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