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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Veröffentlicht in:	Neuroscience Jg. 514; S. 143 - 152
Hauptverfasser:	Franciotti, Raffaella, Nardini, Davide, Russo, Mirella, Onofrj, Marco, Sensi, Stefano L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 15.03.2023
Schlagworte:	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
Online-Zugang:	Volltext
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