A multi-expert ensemble system for predicting Alzheimer transition using clinical features

Abstract

Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that considered only MCI. The system shows higher levels of balanced accuracy, negative predictive value, and specificity than other similar solutions. These results represent a further important step to build a preventive fast-screening machine-learning-based tool to be used as a part of routine healthcare screenings.

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease

“…It is recommended that future research must explore additional modalities not covered in the current study. Additionally, leveraging ensemble learning techniques to combine results from multiple models may be beneficial in investigating the early stages of Alzheimer's disease [ 46 ]. Further Explainable AI (XAI) can be used for the explanation on the reliability and stability of the model used.…”