Modelling the risk of conversion from mild cognitive impairment to Alzheimer’s disease using machine learning
Abderazzak Mouiha, Mohamed Taiebine, Jaouad El HilalyBACKGROUND:
Mild cognitive impairment (MCI) is a transitional state between normal aging and dementia, characterized by subtle cognitive decline. While not all individuals with MCI will progress to Alzheimer’s disease (AD), early identification of those at high risk is crucial for timely interventions. Machine learning algorithms offer a promising approach for analyzing complex datasets, including neuroimaging and cognitive assessments, to predict the likelihood of progression to AD. In this study, we evaluate 12 Machine Learning algorithms to predict the risk of progression from MCI to AD and identify the most important features contributing to this progression.
MATERIALS AND METHODS:
We selected 899 MCI subjects (596 stable, 303 progressive) from the AD Neuroimaging Initiative dataset. ML algorithms were used to predict the progression based on predictive features, including cognitive measures, demographic variables, and neuroimaging biomarkers.
RESULTS:
Linear Discriminant Analysis was identified as the top-performing model, achieving an accuracy of 73.43%, an area under the curve of 0.78, a recall of 69.53%, a precision of 59.38%, and an F1-score of 63.91%. These results demonstrate the model’s ability to effectively identify individuals at risk of progressing to AD. Hippocampal volume and the AD Assessment Scale-Cognitive were identified as the most influential predictors of conversion.
CONCLUSION:
The methodology can be generalized to other types of disease progression, such as transitions from healthy control to MCI or AD. Future studies could incorporate additional biomarkers and longitudinal data to enhance predictive accuracy and disease prevention.