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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Vydané v:Neuroscience Ročník 514; s. 143 - 152
Hlavní autori: Franciotti, Raffaella, Nardini, Davide, Russo, Mirella, Onofrj, Marco, Sensi, Stefano L.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Ltd 15.03.2023
Predmet:
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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Popis
Shrnutí:•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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0306-4522
1873-7544
1873-7544
DOI:10.1016/j.neuroscience.2023.01.029