Using artificial intelligence for predictive analysis of dementia awareness among community adult learners and evaluation of dementia-friendliness in community environments

Abstract

With the rapid advancement of artificial intelligence and information technology, big data analytics have become increasingly applied in healthcare and older adult care. However, in Taiwan, awareness of dementia remains limited and participation in dementia-related programs is low. As the older adult population grows and long-term care budgets become strained, enhancing the awareness of dementia in communities is vital. This study developed a "Dementia Awareness Prediction Model using machine learning to predict the need for dementia education among adult learners, thereby improving resource allocation efficiency. A total of 229 survey responses were collected, and three machine-learning algorithms—Decision Trees, Decision Forests, and Logistic Regression—were used to build predictive models. The results show that all three models effectively predict dementia awareness, with Decision Forests and Logistic Regression demonstrating superior accuracy. Using a reduced set of attributes, the models achieved an average accuracy of over 95.90%, indicating high predictive performance. These findings provide valuable insights for enhancing dementia awareness and optimizing resource distribution in both public and private sectors.