Dementia Deterioration Prediction Using Machine Learning

Dementia is a disease that imposes medical, social, and economic challenges on medical professionals, caregivers, and the patients themselves. Dementia monitoring and prognosis are critical factors besides dementia diagnosis. However, recent studies on dementia prognosis involve people with diagnosed dementia and not non-demented with a high risk of being demented, like people with cognitive difficulties. The aim of this paper is to use Machine Learning (ML) algorithms to predict patients with the risk of deterioration from medical histories containing clinical, cognitive, and profile data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The best model was the Random Forest model with a sensitivity of 0.79, accuracy of 0.77, specificity of 0.76, F1-score of 0.78, and an AUROC of 0.83. Moreover, the model was interpreted through permutation importance. Using the permutation importance tool, the study highlighted the strong effect of diagnosis information and specific symptoms like muscle pain for dementia deterioration prediction.