A multi-expert ensemble system for predicting Alzheimer transition using clinical features

Wolters FJ, Chibnik LB, Waziry R et al (2020) Twenty-seven-year time trends in dementia incidence in Europe and the United States. Neurology 95:e519–e531. https://doi.org/10.1212/WNL.0000000000010022 Zhang XX, Tian Y, Wang ZT et al (2021) The epidemiology of Alzheimer’s disease modifiable risk factors and prevention. J Prev Alzheimer’s Dis 8:313–321 Scheltens P, Strooper BD, Kivipelto M et al (2021) Alzheimer’s disease. Lancet 397:1577–1590 Amieva H, Le Goff M, Millet X et al (2008) Prodromal Alzheimer’s disease: successive emergence of the clinical symptoms. Ann Neurol 64:492–498 Beason-Held LL, Goh JO, An Y et al (2013) Changes in brain function occur years before the onset of cognitive impairment. J Neurosci 33:18008–18014 Rajan KB, Wilson RS, Weuve J et al (2015) Cognitive impairment 18 years before clinical diagnosis of Alzheimer disease dementia. Neurology 85:898–904 Reiman EM, Quiroz YT, Fleisher AS et al (2012) Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: a case-control study. Lancet Neurol 11:1048–1056 Younes L, Albert M, Moghekar A et al (2019) Identifying changepoints in biomarkers during the preclinical phase of Alzheimer’s disease. Front Aging Neurosci 11:74. https://doi.org/10.3389/FNAGI.2019.00074 Isaacson R, Ganzer C, Hristov H et al (2018) The clinical practice of risk reduction for Alzheimer’s disease: a precision medicine approach. Alzheimer’s & Dementia 14:1663–1673 Yiannopoulou KG, Papageorgiou SG (2020) Current and future treatments in Alzheimer disease: an update. J Cent Nervous Syst Dis. https://doi.org/10.1177/1179573520907397 Matthews FE, Stephan BCM, Robinson L et al (2016) A two decade dementia incidence comparison from the Cognitive Function and Ageing Studies I and II. Nat Commun 7:1–8. https://doi.org/10.1038/ncomms11398 Norton S, Matthews F, Barnes D et al (2014) Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurol 13:788–794 Rasmussen J, Langerman H (2019) Alzheimer’s disease—why we need early diagnosis. Degener Neurol Neuromuscul Dis 9:123–130 De Vugt ME, Verhey FR (2013) The impact of early dementia diagnosis and intervention on informal caregivers. Prog Neurobiol 110:54–62 Frias CE, Cabrera E, Zabalegui A (2020) Informal caregivers’ roles in dementia: the impact on their quality of life. Life (Basel) 10:251. https://doi.org/10.3390/life10110251 Petersen R, Parisi J, Dickson D et al (2006) Neuropathologic features of amnestic mild cognitive impairment. Arch Neurol 63:665–672 Roberts R, Knopman D, Mielke M et al (2014) Higher risk of progression to dementia in mild cognitive impairment cases who revert to normal. Neurology 82:317–325 Dukart J, Sambataro F, Bertolino A (2015) Accurate prediction of conversion to Alzheimer’s disease using imaging, genetic, and neuropsychological biomarkers. J Alzheimer’s Dis 49:1143–1159 Caligiore D, Silvetti M, D’Amelio M et al (2020) Computational modeling of catecholamines dysfunction in Alzheimer’s disease at pre-plaque stage. J Alzheimer’s Dis 77:275–290 Grassi M, Rouleaux N, Caldirola D 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. Front Neurol 10:756. https://doi.org/10.3389/fneur.2019.00756 Moustafa AA (2021) Alzheimer’s disease : understanding biomarkers, big data, and therapy. Academic Press, London. ISBN 978-0-12-821334-6 Hampel H, Vergallo A, Perry G et al (2019) The Alzheimer precision medicine initiative. J Alzheimer’s Dis 68:1–24 Perna G, Grassi M, Caldirola D et al (2018) The revolution of personalized psychiatry: will technology make it happen sooner? Psychol Med 48:705–713 Grassi M, Perna G, Caldirola D et al (2018) A clinically-translatable machine learning algorithm for the prediction of Alzheimer’s disease conversion in individuals with mild and premild cognitive impairment. J Alzheimer’s Dis 61:1555–1573 Hojjati S, Ebrahimzadeh A, Khazaee A et al (2017) Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM. J Neurosci Methods 282:69–80 Liu M, Cheng D, Wang K et al (2018) Multi-modality cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Neuroinformatics 16:295–308 Long X, Chen L, Jiang C et al (2017) Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLOS ONE 12:e0173372. https://doi.org/10.1371/JOURNAL.PONE.0173372 Pan D, Zeng A, Jia L et al (2020) Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front Neurosci 14:259. https://doi.org/10.3389/fnins.2020.00259 Platero C, Lin L, Tobar MC (2019) Longitudinal neuroimaging hippocampal markers for diagnosing Alzheimer’s disease. Neuroinformatics 17:43–61 Grueso S, Viejo-Sobera R (2021) Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review. Alzheimer’s Res Ther 13:1–29 Pradhan N, Singh AS, Singh A (2021) Alzheimer disease early diagnosis and prediction using deep learning techniques: a survey. In: Recent trends in communication and electronics, pp 590–593 Odusami M, Maskeliūnas R, Damaševičius R et al (2021) Analysis of features of Alzheimer’s disease: detection of early stage from functional brain changes in magnetic resonance images using a Finetuned ResNet18 Network. Diagnostics 11:1071. https://doi.org/10.3390/diagnostics11061071 Beltran J, Wahba B, Hose N et al (2020) Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer’s Disease Neuroimaging (ADNI) database. PLoS ONE 15:e0235663 Cammisuli DM, Cipriani G, Castelnuovo G (2022) Technological solutions for diagnosis, management and treatment of Alzheimer’s disease-related symptoms: a structured review of the recent scientific literature. Int J Environ Res Public Health. https://doi.org/10.3390/IJERPH19053122 Odusami M, Maskeliūnas R, Damaševičius R (2022) An intelligent system for early recognition of Alzheimerrsquo;s disease using neuroimaging. Sensors. https://doi.org/10.3390/S22030740 Silva-Spínola A, Baldeiras I, Arrais JP et al (2022) The road to personalized medicine in Alzheimer’s disease: the use of artificial intelligence. Biomedicines. https://doi.org/10.3390/BIOMEDICINES10020315 Khanna S, Domingo-Fernández D, Iyappan A et al (2018) Using multi-scale genetic, neuroimaging and clinical data for predicting Alzheimer’s disease and reconstruction of relevant biological mechanisms. Sci Rep. https://doi.org/10.1038/S41598-018-29433-3 Moscoso A, Silva-Rodríguez J, Aldrey JM et al (2019) Prediction of Alzheimer’s disease dementia with MRI beyond the short-term: implications for the design of predictive models. NeuroImage: Clin. https://doi.org/10.1016/j.nicl.2019.101837 Battista P, Salvatore C, Castiglioni I (2017) Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: a machine learning study. Behav Neurol. https://doi.org/10.1155/2017/1850909 Kaplan E, Goodglass H, Weintraub S (1983) Boston naming test. Lea & Febiger, Philadelphia Hughes CP, Berg L, Danziger WL et al (1982) A new clinical scale for the staging of Dementia. Br J Psychiatry 140:566–572 Morris JC (1993) The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43:2412–2414 Pinto E, Peters R (2009) Literature review of the Clock Drawing Test as a tool for cognitive screening. Dementia Geriatr Cognit Disord 27:201–213 Kueper JK, Speechley M, Montero-Odasso M (2018) The Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): modifications and responsiveness in pre-dementia populations. A narrative review. J Alzheimer’s Dis 63:423–444 Rosen W, Mohs R, Davis K (1984) A new rating scale for Alzheimer’s disease. Am J Psychiatry 141:1356–1364 Yesavage JA, Brink TL, Rose TL et al (1982) Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res 17:37–49 Cummings JL, Mega M, Gray K et al (1994) The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology 44:2308–2314 Folstein MF, Robins LN, Helzer JE (1983) The mini-mental state examination. Arch Gen Psychiatry 40:812 Rey A (1964) The clinical psychological examination. Presses Universitaires de France, Paris Reitan RM (1971) Trail making test results for normal and brain-damaged children. Percept Motor Skills 33:575–581 Fonti V, Belitser E (2017) Feature selection using lasso. In: VU Amsterdam Research Paper in Business Analytics 30:1–25 Muthukrishnan R, Rohini R (2016) Lasso: a feature selection technique in predictive modeling for machine learning. In: 2016 IEEE international conference on advances in computer applications (ICACA), IEEE, pp 18–20 Bekkar M, Alitouche TA (2013) Imbalanced data learning approaches review. Int J Data Mining Knowl Manag Process 3:15–33 Cutler A, Cutler DR, Stevens JR (2012) Random forests. In: Ensemble machine learning. Springer, p 157–175 Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge Svensén M, Bishop CM (2007) Pattern recognition and machine learning. Springer, Berlin Abdar M, Zomorodi-Moghadam M, Zhou X et al (2020) A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recogn Lett 132:123–131 Zhong Y, Chalise P, He J (2020) Nested cross-validation with ensemble feature selection and classification model for high-dimensional biological data. In: Communications in statistics-simulation and computation, pp 1–18 Ndiaye E, Le T, Fercoq O, et al (2019) Safe grid search with optimal complexity. In: International conference on machine learning, PMLR, pp 4771–4780 Kyriakides G, Margaritis KG (2019) Hands-on ensemble learning with python: build highly optimized ensemble machine learning models using scikit-learn and Keras. Packt Publishing Ltd, Birmingham Lim WS, Chin JJ, Lam CK et al (2005) Clinical dementia rating experience of a multi-racial Asian population. Alzheimer Dis Assoc Disord 19:135–142 Lee YM, Park JM, Lee BD et al (2012) Memory impairment, in mild cognitive impairment without significant cerebrovascular disease, predicts progression to Alzheimer’s disease. Dementia Geriatr Cognit Disord 33:240–244 Grober E, Cb Hall, Lipton RB et al (2008) Memory impairment, executive dysfunction, and intellectual decline in preclinical Alzheimer’s disease. J Int Neuropsychol Soc 14:266–278 Luukinen H, Viramo P, Koski K et al (1999) Head injuries and cognitive decline among older adults a population-based study. Neurology 52:557–557 Whiteneck GG, Gerhart KA, Cusick CP (2004) Identifying environmental factors that influence the outcomes of people with traumatic brain injury. J Head Trauma Rehabil 19:191–204 Plassman BL, Havlik RJ, Steffens DC et al (2000) Documented head injury in early adulthood and risk of Alzheimer’s disease and other dementias. Neurology 55:1158–1166 Rasmusson D, Brandt J, Martin D et al (1995) Head injury as a risk factor in Alzheimer’s disease. Brain Inj 9:213–219 Schofield P, Tang M, Marder K et al (1997) Alzheimer’s disease after remote head injury: an incidence study. J Neurol Neurosurg Psychiatry 62:119–124 Sivanandam TM, Thakur MK (2012) Traumatic brain injury: a risk factor for Alzheimer’s disease. Neurosci Biobehav Rev 36:1376–1381 Etgen T (2015) Kidney disease as a determinant of cognitive decline and dementia. Alzheimer’s Res Ther 7:29. https://doi.org/10.1186/s13195-015-0115-4 Buchman AS, Tanne D, Boyle PA et al (2009) Kidney function is associated with the rate of cognitive decline in the elderly. Neurology 73:920–927 Braga-Neto P, Pedroso JL, Alessi H et al (2013) Early-onset familial Alzheimer’s disease related to presenilin 1 mutation resembling autosomal dominant spinocerebellar ataxia. J Neurol 260:1177–1179 Testi S, Peluso S, Fabrizi GM et al (2014) A novel PSEN1 mutation in a patient with sporadic early-onset Alzheimer’s disease and prominent cerebellar ataxia. J Alzheimer’s Dis 41:709–714 Jacobs HIL, Hopkins DA, Mayrhofer HC et al (2018) The cerebellum in Alzheimer’s disease: evaluating its role in cognitive decline. Brain 141:37–47 Caligiore D, Helmich RC, Hallett M et al (2016) Parkinson’s disease as a system-level disorder. NPJ Parkinson’s Dis 2:1–9. https://doi.org/10.1038/npjparkd.2016.25 Jo T, Nho K, Risacher SL et al (2020) Deep learning detection of informative features in tau PET for Alzheimer’s disease classification. BMC Bioinform. https://doi.org/10.1186/S12859-020-03848-0 Lin CH, Chiu SI, Chen TF et al (2020) Classifications of neurodegenerative disorders using a multiplex blood biomarkers-based machine learning model. Int J Mol Sci 21:1–15 Nguyen DT, Ryu S, Qureshi MNI et al (2019) Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer’s dementia diagnosis using multi-measure rs-fMRI spatial patterns. PLOS ONE. https://doi.org/10.1371/JOURNAL.PONE.0212582 Nunes A, Silva G, Duque C et al (2019) Retinal texture biomarkers may help to discriminate between Alzheimer’s, Parkinson’s, and healthy controls. PLoS ONE. https://doi.org/10.1371/JOURNAL.PONE.0218826 Clute-Reinig N, Jayadev S, Rhoads K et al (2021) Alzheimer’s disease diagnostics must be globally accessible. J Alzheimer’s Dis 84:1453–1455