Comparison of Machine Learning-based Approaches to Predict the Conversion to Alzheimer’s Disease from Mild Cognitive Impairment

•Psychological and AD-related biomarkers reach the highest accuracy in AD prediction.•Clinical and biological data are helpful in machine learning algorithms’ performances.•Multiple machine learning algorithms make more accurate and solid AD predictions. In Mild Cognitive Impairment (MCI), identifyi...

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Vydáno v:	Neuroscience Ročník 514; s. 143 - 152
Hlavní autoři:	Franciotti, Raffaella, Nardini, Davide, Russo, Mirella, Onofrj, Marco, Sensi, Stefano L.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 15.03.2023
Témata:	Alzheimer Disease - cerebrospinal fluid Alzheimer Disease - diagnostic imaging Alzheimer’s disease dementia (AD) prediction artificial intelligence Biomarkers - cerebrospinal fluid Cognitive Dysfunction - cerebrospinal fluid Cognitive Dysfunction - diagnosis Disease Progression gradient boosting Humans Machine Learning machine learning (ML) algorithms Magnetic Resonance Imaging - methods mild cognitive impairment (MCI) random forest (RF) ADNI c-MCI ABETA sMRI artificial intelligence random forest (RF) NPV PPV t-Tau GB LM-DEL PTAU ML machine learning (ML) algorithms RAVLT-DEL and -IMM SHAP AD mild cognitive impairment (MCI) p-Tau ADAS-Cog-11 and 13 Alzheimer’s disease dementia (AD) prediction AdaBoost MCI XGB gradient boosting s-MCI RF FAQ
ISSN:	0306-4522, 1873-7544, 1873-7544
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Abstract	•Psychological and AD-related biomarkers reach the highest accuracy in AD prediction.•Clinical and biological data are helpful in machine learning algorithms’ performances.•Multiple machine learning algorithms make more accurate and solid AD predictions. In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer’s Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms –when applied to different biomarkers and other conditions– is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed –with the voting procedure– to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms’ performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions.
AbstractList	In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer's Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms -when applied to different biomarkers and other conditions- is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed -with the voting procedure- to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms' performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions. •Psychological and AD-related biomarkers reach the highest accuracy in AD prediction.•Clinical and biological data are helpful in machine learning algorithms’ performances.•Multiple machine learning algorithms make more accurate and solid AD predictions. In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer’s Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms –when applied to different biomarkers and other conditions– is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed –with the voting procedure– to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms’ performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions. In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer's Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms -when applied to different biomarkers and other conditions- is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed -with the voting procedure- to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms' performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions.In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer's Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms -when applied to different biomarkers and other conditions- is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed -with the voting procedure- to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms' performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions.
Author	Sensi, Stefano L. Franciotti, Raffaella Russo, Mirella Nardini, Davide Onofrj, Marco
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Cites_doi	10.1001/archneur.63.5.693 10.30534/ijatcse/2019/0581.42019 10.1177/0020720921989015 10.1016/j.jalz.2011.03.008 10.1186/s40537-020-00327-4 10.1002/alz.12645 10.1016/j.pscychresns.2011.06.014 10.1007/s00521-016-2416-3 10.1038/s41746-022-00592-y 10.1108/03684921311295547 10.1023/A:1010933404324 10.3233/JAD-210573 10.1016/j.neurobiolaging.2018.10.004 10.1016/j.neuroimage.2006.01.021 10.1111/j.1742-4658.2011.08228.x 10.1007/s12525-021-00475-2 10.1016/j.mayocp.2014.06.019 10.1212/WNL.0000000000004569 10.1186/1750-1326-9-53 10.1016/j.trci.2019.11.002 10.1186/s12883-020-01728-x 10.1016/j.nicl.2017.10.009 10.1016/j.neuron.2014.05.004 10.1111/joim.12190 10.3389/fnagi.2013.00058 10.3389/fneur.2020.576029 10.3390/informatics8040079 10.3390/rs14153547 10.1016/j.tins.2018.09.009 10.18632/aging.101719 10.1007/s00500-020-05292-x
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Keywords	ADNI c-MCI ABETA sMRI artificial intelligence random forest (RF) NPV PPV t-Tau GB LM-DEL PTAU ML machine learning (ML) algorithms RAVLT-DEL and -IMM SHAP AD mild cognitive impairment (MCI) p-Tau ADAS-Cog-11 and 13 Alzheimer’s disease dementia (AD) prediction AdaBoost MCI XGB gradient boosting s-MCI RF FAQ
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References	Schapire, Freund (b0155) 2013 Desikan, Ségonne, Fischl, Quinn, Dickerson, Blacker, Buckner, Dale, Maguire, Hyman, Albert, Killiany (b0060) 2006; 31 Brem, Sensi (b0025) 2018; 41 Poil, de Haan, van der Flier, Mansvelder, Scheltens, Linkenkaer-Hansen (b0135) 2013; 5 Shree, Sheshadri (b0160) 2018; 29 Casaletto, Elahi, Bettcher, Neuhaus, Bendlin, Asthana (b0035) 2017; 89 Natras, Soja, Schmidt (b0125) 2022; 14 Spires-Jones, Hyman (b0165) 2014; 82 Massetti, Russo, Franciotti, Nardini, Mandolini, Granzotto, Bomba, Delli Pizzi, Mosca, Scherer, Onofrj, Sensi, Alzheimer’s Disease Neuroimaging Initiative (ADNI); Alzheimer’s Disease Metabolomics Consortium (ADMC) (b0110) 2022; 85 Faouri, AlBashayreh, Azzeh (b0075) 2022; 333–346 Williams, Serpell (b0190) 2011; 278 Delli Pizzi, Punzi, Sensi, Initiative (b0055) 2019; 74 Apostolova, Dutton, Dinov, Hayashi, Toga, Cummings, Thompson (b0010) 2006; 63 Syaifullah, Shiino, Kitahara, Ito, Ishida, Tanigaki (b0170) 2021; 11 Brinkmalm, Brinkmalm, Honer, Fr€olich, Hausner, Minthon, Hansson, Wallin, Zetterberg, Blennow, Öhrfelt (b0030) 2014; 9 Vozella, Basit, Piras, Realini, Armirotti, Bossù, Assogna, Sensi, Spalletta, Piomelli (b0185) 2019; 11 Chen, Dewi, Huang, Caraka (b0040) 2020; 7 Dinu, Ganesan (b0065) 2019; 8 Cox, Li, Darlington (b0050) 2001; 49 Petersen, Caracciolo, Brayne, Gauthier, Jelic, Fratiglioni (b0130) 2014; 275 Chen, Guestrin (b0045) 2016 Fernández-Delgado, Cernadas, Barro, Amorim (b0080) 2014; 15 Poulin, Dautoff, Morris, Barrett, Dickerson, Initiative (b0140) 2011; 194 Rohini, Surendran (b9000) 2021; 25 Janiesch, Zschech, Heinrich (b0095) 2021; 31 Galasko, Xiao, Xu, Smirnov, Salmon, Dewit, Vanbrabant, Jacobs, Vanderstichele, Vanmechelen, Initiative, ADNI, Worley (b0090) 2019; 5 Mazaheri, Segaert, Olichney, Yang, Niu, Shapiro, Bowman (b0115) 2018; 17 Bergstra, Bengio (b0015) 2012; 13 Albert, DeKosky, Dickson, Dubois, Feldman, Fox, Gamst, Holtzman, Jagust, Petersen, Snyder, Carrillo, Thies, Phelps (b0005) 2011; 7 Rossini, Miraglia, Vecchio (b0150) 2022; 18 Friedman (b0085) 2001 Mishra, Mallick, Tripathy, Jena, Chae (b0120) 2021 Knopman, Petersen (b0100) 2014; 89 Qi (b0145) 2012 Teng, Li, Zhao, Hu, Zhang, Yao, Hu, Alzheimer’ s Disease Neuroimaging Initiative (ADNI) (b0175) 2020; 20 Elgeldawi, Sayed, Galal, Zaki (b0070) 2021; 8 Breiman (b0020) 2001; 45 Lundberg, Lee (b0105) 2017; 30 Varoquaux, Cheplygina (b0180) 2022; 5 Delli Pizzi (10.1016/j.neuroscience.2023.01.029_b0055) 2019; 74 Qi (10.1016/j.neuroscience.2023.01.029_b0145) 2012 Faouri (10.1016/j.neuroscience.2023.01.029_b0075) 2022; 333–346 Albert (10.1016/j.neuroscience.2023.01.029_b0005) 2011; 7 Breiman (10.1016/j.neuroscience.2023.01.029_b0020) 2001; 45 Vozella (10.1016/j.neuroscience.2023.01.029_b0185) 2019; 11 Brem (10.1016/j.neuroscience.2023.01.029_b0025) 2018; 41 Brinkmalm (10.1016/j.neuroscience.2023.01.029_b0030) 2014; 9 Massetti (10.1016/j.neuroscience.2023.01.029_b0110) 2022; 85 Rossini (10.1016/j.neuroscience.2023.01.029_b0150) 2022; 18 Fernández-Delgado (10.1016/j.neuroscience.2023.01.029_b0080) 2014; 15 Dinu (10.1016/j.neuroscience.2023.01.029_b0065) 2019; 8 Poil (10.1016/j.neuroscience.2023.01.029_b0135) 2013; 5 Bergstra (10.1016/j.neuroscience.2023.01.029_b0015) 2012; 13 Poulin (10.1016/j.neuroscience.2023.01.029_b0140) 2011; 194 Desikan (10.1016/j.neuroscience.2023.01.029_b0060) 2006; 31 Shree (10.1016/j.neuroscience.2023.01.029_b0160) 2018; 29 Mazaheri (10.1016/j.neuroscience.2023.01.029_b0115) 2018; 17 Schapire (10.1016/j.neuroscience.2023.01.029_b0155) 2013 Lundberg (10.1016/j.neuroscience.2023.01.029_b0105) 2017; 30 Rohini (10.1016/j.neuroscience.2023.01.029_b9000) 2021; 25 Chen (10.1016/j.neuroscience.2023.01.029_b0045) 2016 Natras (10.1016/j.neuroscience.2023.01.029_b0125) 2022; 14 Cox (10.1016/j.neuroscience.2023.01.029_b0050) 2001; 49 Williams (10.1016/j.neuroscience.2023.01.029_b0190) 2011; 278 Galasko (10.1016/j.neuroscience.2023.01.029_b0090) 2019; 5 Janiesch (10.1016/j.neuroscience.2023.01.029_b0095) 2021; 31 Apostolova (10.1016/j.neuroscience.2023.01.029_b0010) 2006; 63 Casaletto (10.1016/j.neuroscience.2023.01.029_b0035) 2017; 89 Friedman (10.1016/j.neuroscience.2023.01.029_b0085) 2001 Mishra (10.1016/j.neuroscience.2023.01.029_b0120) 2021 Petersen (10.1016/j.neuroscience.2023.01.029_b0130) 2014; 275 Knopman (10.1016/j.neuroscience.2023.01.029_b0100) 2014; 89 Varoquaux (10.1016/j.neuroscience.2023.01.029_b0180) 2022; 5 Chen (10.1016/j.neuroscience.2023.01.029_b0040) 2020; 7 Spires-Jones (10.1016/j.neuroscience.2023.01.029_b0165) 2014; 82 Teng (10.1016/j.neuroscience.2023.01.029_b0175) 2020; 20 Elgeldawi (10.1016/j.neuroscience.2023.01.029_b0070) 2021; 8 Syaifullah (10.1016/j.neuroscience.2023.01.029_b0170) 2021; 11 36592944 - Neuroscience. 2023 Mar 15;514:141-142
References_xml	– volume: 11 start-page: 73 year: 2019 end-page: 88 ident: b0185 article-title: Elevated plasma ceramide levels in post-menopausal women: a crosssectional study publication-title: Aging (Albany NY) – volume: 5 start-page: 48 year: 2022 ident: b0180 article-title: Machine learning for medical imaging: methodological failures and recommendations for the future publication-title: npj Digit Med – volume: 17 start-page: 188 year: 2018 end-page: 197 ident: b0115 article-title: EEG oscillations during word processing predict MCI conversion to Alzheimer’s disease publication-title: NeuroImage: Clin – volume: 333–346 year: 2022 ident: b0075 article-title: Examining stability of machine learning methods for predicting dementia at early phases of the disease publication-title: Decis Sci Lett – volume: 194 start-page: 7 year: 2011 end-page: 13 ident: b0140 article-title: Amygdala atrophy is prominent in early Alzheimer’s disease and relates to symptom severity publication-title: Psychiatry Res Neuroimaging – volume: 8 year: 2021 ident: b0070 article-title: Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis publication-title: Informatics – volume: 85 start-page: 1639 year: 2022 end-page: 1655 ident: b0110 article-title: A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer’s Disease Spectrum publication-title: J Alzheimer’s Dis – volume: 11 year: 2021 ident: b0170 article-title: Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation publication-title: Front Neurol – start-page: 307 year: 2012 end-page: 323 ident: b0145 article-title: Random Forest for Bioinformatics publication-title: Ensemble Machine Learning – volume: 63 start-page: 693 year: 2006 end-page: 699 ident: b0010 article-title: Conversion of Mild Cognitive Impairment to Alzheimer Disease Predicted by Hippocampal Atrophy Maps publication-title: Arch Neurol – volume: 14 start-page: 3547 year: 2022 ident: b0125 article-title: Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting publication-title: Remote Sens – volume: 275 start-page: 214 year: 2014 end-page: 228 ident: b0130 article-title: Mild cognitive impairment: a concept in evolution publication-title: J Intern Med – volume: 89 start-page: 1782 year: 2017 end-page: 1788 ident: b0035 article-title: Neurogranin, a synaptic protein, is associated with memory independent of Alzheimer biomarkers publication-title: Neurology – volume: 31 start-page: 968 year: 2006 end-page: 980 ident: b0060 article-title: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest publication-title: Neuroimage – volume: 7 start-page: 270 year: 2011 end-page: 279 ident: b0005 article-title: The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease publication-title: Alzheimers Dement – volume: 8 start-page: 29 year: 2019 end-page: 37 ident: b0065 article-title: Early detection of Alzheimer’s disease using predictive k-NN instance based approach and T-Test Method publication-title: Int J Adv Trends Comput Sci Eng – volume: 15 start-page: 3133 year: 2014 end-page: 3181 ident: b0080 article-title: Do we need hundreds of classifiers to solve real-world classification problems? publication-title: J Mach Learn Res – volume: 49 start-page: 219 year: 2001 end-page: 230 ident: b0050 article-title: Extracting room reverberation time from speech using artificial neural networks publication-title: J audio Eng. Soc – volume: 20 start-page: 148 year: 2020 ident: b0175 article-title: Predicting MCI progression with FDG-PET and cognitive scores: a longitudinal study publication-title: BMC Neurol – start-page: 1189 year: 2001 end-page: 1232 ident: b0085 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann Stat – year: 2013 ident: b0155 article-title: Boosting: Foundations and algorithms publication-title: Kybernetes. – volume: 29 start-page: 123 year: 2018 end-page: 132 ident: b0160 article-title: Diagnosis of Alzheimer’s disease using Naive Bayesian Classifiers publication-title: Neural Comput & Applic – volume: 25 start-page: 2589 year: 2021 end-page: 2597 ident: b9000 article-title: Toward Alzheimer’s disease classification through machine learning publication-title: Soft Comput – volume: 7 start-page: 1 year: 2020 end-page: 26 ident: b0040 article-title: Selecting critical features for data classification based on machine learning methods publication-title: J Big Data – volume: 13 start-page: 281 year: 2012 end-page: 305 ident: b0015 article-title: Random search for hyper-parameter optimization publication-title: J Mach Learn Res – volume: 89 start-page: 1452 year: 2014 end-page: 1459 ident: b0100 article-title: Mild Cognitive Impairment and Mild Dementia: A Clinical Perspective publication-title: Mayo Clin Proc – volume: 18 start-page: 2699 year: 2022 end-page: 2706 ident: b0150 article-title: Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learningmethods for feature extraction from integrated biomarkers, in particular for EEG signal analysis publication-title: Alzheimer’s Dement – volume: 5 start-page: 58 year: 2013 ident: b0135 article-title: Integrative EEG biomarkers predict progression to Alzheimer’s disease at the MCI stage publication-title: Front Aging Neurosci – volume: 31 start-page: 685 year: 2021 end-page: 695 ident: b0095 article-title: Machine learning and deep learning publication-title: Electronic Markets – year: 2021 ident: b0120 article-title: Stacked KNN with hard voting predictive approach to assist the hiring process in IT organizations publication-title: Int J Electr Eng Educ – volume: 5 start-page: 871 year: 2019 end-page: 882 ident: b0090 article-title: Synaptic biomarkers in CSF aid in diagnosis, correlate with cognition and predict progression in MCI and Alzheimer’s disease publication-title: Alzheimers Dement (N Y) – volume: 41 start-page: 885 year: 2018 end-page: 897 ident: b0025 article-title: Towards Combinatorial Approaches for Preserving Cognitive Fitness in Aging publication-title: Trends Neurosci – volume: 74 start-page: 21 year: 2019 end-page: 37 ident: b0055 article-title: Functional signature of conversion of patients with mild cognitive impairment publication-title: Neurobiol Aging – volume: 278 start-page: 3905 year: 2011 end-page: 3917 ident: b0190 article-title: Membrane and surface interactions of the Alzheimer’s A! peptide: Insights into the mechanism of cytotoxicity publication-title: FEBS J – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: b0020 article-title: Random Forests publication-title: Mach Learn – volume: 30 year: 2017 ident: b0105 article-title: A unified approach to interpreting model predictions publication-title: Adv Neural Inf Process Syst – year: 2016 ident: b0045 article-title: XGBoost publication-title: in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (ACM) – volume: 9 start-page: 53 year: 2014 ident: b0030 article-title: SNAP-25 is a promising novel cerebrospinal fluid biomarker for synapse degeneration in Alzheimer’s disease publication-title: Mol Neurodegener – volume: 82 start-page: 756 year: 2014 end-page: 771 ident: b0165 article-title: The intersection of amyloid beta and tau at synapses in Alzheimer’s disease publication-title: Neuron – volume: 63 start-page: 693 year: 2006 ident: 10.1016/j.neuroscience.2023.01.029_b0010 article-title: Conversion of Mild Cognitive Impairment to Alzheimer Disease Predicted by Hippocampal Atrophy Maps publication-title: Arch Neurol doi: 10.1001/archneur.63.5.693 – volume: 8 start-page: 29 year: 2019 ident: 10.1016/j.neuroscience.2023.01.029_b0065 article-title: Early detection of Alzheimer’s disease using predictive k-NN instance based approach and T-Test Method publication-title: Int J Adv Trends Comput Sci Eng doi: 10.30534/ijatcse/2019/0581.42019 – year: 2021 ident: 10.1016/j.neuroscience.2023.01.029_b0120 article-title: Stacked KNN with hard voting predictive approach to assist the hiring process in IT organizations publication-title: Int J Electr Eng Educ doi: 10.1177/0020720921989015 – volume: 7 start-page: 270 year: 2011 ident: 10.1016/j.neuroscience.2023.01.029_b0005 article-title: The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease publication-title: Alzheimers Dement doi: 10.1016/j.jalz.2011.03.008 – volume: 30 year: 2017 ident: 10.1016/j.neuroscience.2023.01.029_b0105 article-title: A unified approach to interpreting model predictions publication-title: Adv Neural Inf Process Syst – volume: 7 start-page: 1 year: 2020 ident: 10.1016/j.neuroscience.2023.01.029_b0040 article-title: Selecting critical features for data classification based on machine learning methods publication-title: J Big Data doi: 10.1186/s40537-020-00327-4 – volume: 18 start-page: 2699 year: 2022 ident: 10.1016/j.neuroscience.2023.01.029_b0150 article-title: Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learningmethods for feature extraction from integrated biomarkers, in particular for EEG signal analysis publication-title: Alzheimer’s Dement doi: 10.1002/alz.12645 – volume: 194 start-page: 7 year: 2011 ident: 10.1016/j.neuroscience.2023.01.029_b0140 article-title: Amygdala atrophy is prominent in early Alzheimer’s disease and relates to symptom severity publication-title: Psychiatry Res Neuroimaging doi: 10.1016/j.pscychresns.2011.06.014 – volume: 29 start-page: 123 year: 2018 ident: 10.1016/j.neuroscience.2023.01.029_b0160 article-title: Diagnosis of Alzheimer’s disease using Naive Bayesian Classifiers publication-title: Neural Comput & Applic doi: 10.1007/s00521-016-2416-3 – volume: 5 start-page: 48 year: 2022 ident: 10.1016/j.neuroscience.2023.01.029_b0180 article-title: Machine learning for medical imaging: methodological failures and recommendations for the future publication-title: npj Digit Med doi: 10.1038/s41746-022-00592-y – year: 2013 ident: 10.1016/j.neuroscience.2023.01.029_b0155 article-title: Boosting: Foundations and algorithms publication-title: Kybernetes. doi: 10.1108/03684921311295547 – volume: 45 start-page: 5 year: 2001 ident: 10.1016/j.neuroscience.2023.01.029_b0020 article-title: Random Forests publication-title: Mach Learn doi: 10.1023/A:1010933404324 – volume: 15 start-page: 3133 year: 2014 ident: 10.1016/j.neuroscience.2023.01.029_b0080 article-title: Do we need hundreds of classifiers to solve real-world classification problems? publication-title: J Mach Learn Res – volume: 85 start-page: 1639 year: 2022 ident: 10.1016/j.neuroscience.2023.01.029_b0110 article-title: A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer’s Disease Spectrum publication-title: J Alzheimer’s Dis doi: 10.3233/JAD-210573 – volume: 74 start-page: 21 year: 2019 ident: 10.1016/j.neuroscience.2023.01.029_b0055 article-title: Functional signature of conversion of patients with mild cognitive impairment publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2018.10.004 – volume: 31 start-page: 968 year: 2006 ident: 10.1016/j.neuroscience.2023.01.029_b0060 article-title: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest publication-title: Neuroimage doi: 10.1016/j.neuroimage.2006.01.021 – volume: 49 start-page: 219 year: 2001 ident: 10.1016/j.neuroscience.2023.01.029_b0050 article-title: Extracting room reverberation time from speech using artificial neural networks publication-title: J audio Eng. Soc – volume: 278 start-page: 3905 year: 2011 ident: 10.1016/j.neuroscience.2023.01.029_b0190 article-title: Membrane and surface interactions of the Alzheimer’s A! peptide: Insights into the mechanism of cytotoxicity publication-title: FEBS J doi: 10.1111/j.1742-4658.2011.08228.x – volume: 31 start-page: 685 year: 2021 ident: 10.1016/j.neuroscience.2023.01.029_b0095 article-title: Machine learning and deep learning publication-title: Electronic Markets doi: 10.1007/s12525-021-00475-2 – start-page: 1189 year: 2001 ident: 10.1016/j.neuroscience.2023.01.029_b0085 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann Stat – volume: 89 start-page: 1452 year: 2014 ident: 10.1016/j.neuroscience.2023.01.029_b0100 article-title: Mild Cognitive Impairment and Mild Dementia: A Clinical Perspective publication-title: Mayo Clin Proc doi: 10.1016/j.mayocp.2014.06.019 – volume: 89 start-page: 1782 year: 2017 ident: 10.1016/j.neuroscience.2023.01.029_b0035 article-title: Neurogranin, a synaptic protein, is associated with memory independent of Alzheimer biomarkers publication-title: Neurology doi: 10.1212/WNL.0000000000004569 – volume: 9 start-page: 53 year: 2014 ident: 10.1016/j.neuroscience.2023.01.029_b0030 article-title: SNAP-25 is a promising novel cerebrospinal fluid biomarker for synapse degeneration in Alzheimer’s disease publication-title: Mol Neurodegener doi: 10.1186/1750-1326-9-53 – volume: 5 start-page: 871 year: 2019 ident: 10.1016/j.neuroscience.2023.01.029_b0090 article-title: Synaptic biomarkers in CSF aid in diagnosis, correlate with cognition and predict progression in MCI and Alzheimer’s disease publication-title: Alzheimers Dement (N Y) doi: 10.1016/j.trci.2019.11.002 – year: 2016 ident: 10.1016/j.neuroscience.2023.01.029_b0045 article-title: XGBoost – volume: 20 start-page: 148 year: 2020 ident: 10.1016/j.neuroscience.2023.01.029_b0175 article-title: Predicting MCI progression with FDG-PET and cognitive scores: a longitudinal study publication-title: BMC Neurol doi: 10.1186/s12883-020-01728-x – start-page: 307 year: 2012 ident: 10.1016/j.neuroscience.2023.01.029_b0145 article-title: Random Forest for Bioinformatics – volume: 17 start-page: 188 year: 2018 ident: 10.1016/j.neuroscience.2023.01.029_b0115 article-title: EEG oscillations during word processing predict MCI conversion to Alzheimer’s disease publication-title: NeuroImage: Clin doi: 10.1016/j.nicl.2017.10.009 – volume: 82 start-page: 756 year: 2014 ident: 10.1016/j.neuroscience.2023.01.029_b0165 article-title: The intersection of amyloid beta and tau at synapses in Alzheimer’s disease publication-title: Neuron doi: 10.1016/j.neuron.2014.05.004 – volume: 333–346 year: 2022 ident: 10.1016/j.neuroscience.2023.01.029_b0075 article-title: Examining stability of machine learning methods for predicting dementia at early phases of the disease publication-title: Decis Sci Lett – volume: 275 start-page: 214 year: 2014 ident: 10.1016/j.neuroscience.2023.01.029_b0130 article-title: Mild cognitive impairment: a concept in evolution publication-title: J Intern Med doi: 10.1111/joim.12190 – volume: 5 start-page: 58 year: 2013 ident: 10.1016/j.neuroscience.2023.01.029_b0135 article-title: Integrative EEG biomarkers predict progression to Alzheimer’s disease at the MCI stage publication-title: Front Aging Neurosci doi: 10.3389/fnagi.2013.00058 – volume: 11 year: 2021 ident: 10.1016/j.neuroscience.2023.01.029_b0170 article-title: Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation publication-title: Front Neurol doi: 10.3389/fneur.2020.576029 – volume: 8 year: 2021 ident: 10.1016/j.neuroscience.2023.01.029_b0070 article-title: Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis publication-title: Informatics doi: 10.3390/informatics8040079 – volume: 14 start-page: 3547 year: 2022 ident: 10.1016/j.neuroscience.2023.01.029_b0125 article-title: Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting publication-title: Remote Sens doi: 10.3390/rs14153547 – volume: 41 start-page: 885 year: 2018 ident: 10.1016/j.neuroscience.2023.01.029_b0025 article-title: Towards Combinatorial Approaches for Preserving Cognitive Fitness in Aging publication-title: Trends Neurosci doi: 10.1016/j.tins.2018.09.009 – volume: 13 start-page: 281 year: 2012 ident: 10.1016/j.neuroscience.2023.01.029_b0015 article-title: Random search for hyper-parameter optimization publication-title: J Mach Learn Res – volume: 11 start-page: 73 year: 2019 ident: 10.1016/j.neuroscience.2023.01.029_b0185 article-title: Elevated plasma ceramide levels in post-menopausal women: a crosssectional study publication-title: Aging (Albany NY) doi: 10.18632/aging.101719 – volume: 25 start-page: 2589 year: 2021 ident: 10.1016/j.neuroscience.2023.01.029_b9000 article-title: Toward Alzheimer’s disease classification through machine learning publication-title: Soft Comput doi: 10.1007/s00500-020-05292-x – reference: 36592944 - Neuroscience. 2023 Mar 15;514:141-142
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Snippet	•Psychological and AD-related biomarkers reach the highest accuracy in AD prediction.•Clinical and biological data are helpful in machine learning algorithms’... In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer's Disease Dementia (AD) is a primary goal for patient management....
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SubjectTerms	Alzheimer Disease - cerebrospinal fluid Alzheimer Disease - diagnostic imaging Alzheimer’s disease dementia (AD) prediction artificial intelligence Biomarkers - cerebrospinal fluid Cognitive Dysfunction - cerebrospinal fluid Cognitive Dysfunction - diagnosis Disease Progression gradient boosting Humans Machine Learning machine learning (ML) algorithms Magnetic Resonance Imaging - methods mild cognitive impairment (MCI) random forest (RF)
Title	Comparison of Machine Learning-based Approaches to Predict the Conversion to Alzheimer’s Disease from Mild Cognitive Impairment
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