A novelty detection approach to effectively predict conversion from mild cognitive impairment to Alzheimer’s disease

Abstract

Accurately recognising patients with progressive mild cognitive impairment (pMCI) who will develop Alzheimer’s disease (AD) in subsequent years is very important, as early identification of those patients will enable interventions to potentially reduce the number of those transitioning from MCI to AD. Most studies in this area have concentrated on high-dimensional neuroimaging data with supervised binary/multi-class classification algorithms. However, neuroimaging data is more costly to obtain than non-imaging, and healthcare datasets are normally imbalanced which may reduce classification performance and reliability. To address these challenges, we proposed a new strategy that employs unsupervised novelty detection (ND) techniques to predict pMCI from the AD neuroimaging initiative non-imaging data. ND algorithms, including the k-nearest neighbours (kNN), k-means, Gaussian mixture model (GMM), isolation forest (IF) and extreme learning machine (ELM), were employed and compared with supervised binary support vector machine (SVM) and random forest (RF). We introduced optimisation with nested cross-validation and focused on maximising the adjusted F measure to ensure maximum generalisation of the proposed system by minimising false negative rates. Our extensive experimental results show that ND algorithms (0.727 ± 0.029 kNN, 0.7179 ± 0.0523 GMM, 0.7276 ± 0.0281 ELM) obtained comparable performance to supervised binary SVM (0.7359 ± 0.0451) with 20% stable MCI misclassification tolerance and were significantly better than RF (0.4771 ± 0.0167). Moreover, we found that the non-invasive, readily obtainable, and cost-effective cognitive and functional assessment was the most efficient predictor for predicting the pMCI within 2 years with ND techniques. Importantly, we presented an accessible and cost-effective approach to pMCI prediction, which does not require labelled data.

Predicting Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using K-Means Clustering on MRI Data

“…To obtain results, in most cases researchers exploited the predictive capabilities of standard Machine Learning (ML) methods together with a mere statistical analysis of the data at different time scans. As an example, both in [16,21] and in [6,22] the authors used Support Vector Machine (SVM) applied to brain network graphs to detect MCI-to-AD subjects using features computed from local and global graph measures, with high percentages of accuracy, sensitivity, and specificity. On the other hand, we can say that the difference in percentage can also occur when a different cohort of patients and methodology are used: in [20], the authors investigate whether the combination of fluoro-2deoxy-d-glucose and PET measures with the APOE genotype would improve prediction of the MCI-to-AD conversion.…”

Novelty detection technology for Alzheimer's disease based on autoencoder feature extraction and MCD classification

“…Machine learning-based diagnostic decision systems held significant potential for generating substantial economic benefits. However, training machine learning algorithms [10] for clinical applications often posed challenges. Firstly, imbalanced data distributions might lead to inflated performance estimates for traditional binary/multiclass classifiers [11] .…”