Dementia prediction with multimodal clinical and imaging data

Abstract Dementia affects millions of people worldwide, and poses significant challenges due to its irreversible nature and a lack of effective treatment options. Dementia has a considerable influence on people and society and puts a heavy burden on the healthcare systems. This underscores an urgent need for proactive measures to address this public health concern through early detection and intervention. This paper investigates the use of machine learning for an early detection of dementia and its progression utilizing a public dataset. Various traditional machine learning algorithms, were used on the demographic data, with the Gaussian Naïve Bayes achieving the highest accuracy of 91.30%. Four deep learning models, ResNet50, DenseNet121, VGG16, and Inceptionv3 were used on image data, with the DenseNet121 model achieving the highest accuracy of 90%. We also used SHapley Additive exPlanations (SHAP) framework for dementia progression which revealed that Normalised Whole Brain Volume (nWBV) exhibited higher variability in their impact across models. This study demonstrates the potential of machine learning approaches for early dementia detection and prognosis, which can have significant effect in patient care strategies.