Screening dementia and predicting high dementia risk groups using machine learning

Abstract

New technologies such as artificial intelligence, the internet of things, big data, and cloud computing have changed the overall society and economy, and the medical field particularly has tried to combine traditional examination methods and new technologies. The most remarkable field in medical research is the technology of predicting high dementia risk group using big data and artificial intelligence. This review introduces: (1) the definition, main concepts, and classification of machine learning and overall distinction of it from traditional statistical analysis models; and (2) the latest studies in mental science to detect dementia and predict high-risk groups in order to help competent researchers who are challenging medical artificial intelligence in the field of psychiatry. As a result of reviewing 4 studies that used machine learning to discriminate high-risk groups of dementia, various machine learning algorithms such as boosting model, artificial neural network, and random forest were used for predicting dementia. The development of machine learning algorithms will change primary care by applying advanced machine learning algorithms to detect high dementia risk groups in the future.

Keywords: Artificial intelligence; Clinical decision support system; Dementia; Machine learning; Mild cognitive impairment.

Figure 1

Diagram for concepts of artificial intelligence, deep learning and machine learning. KNN: K-nearest neighbors; SVM: Support vector machine; RNN: Recurrent neural network; MLP: Multilayer perceptron; CNN: Convolutional neural network.

Figure 2

The concept of two validations. A: The concept of hold-out validation; B: The concept of k-fold validation.