Artificial Intelligence Platform on Semantic Knowledge Base for Recommending Elderly with Chronic Diseases Care
Keywords:
platform, Artificial Intelligence, Semantic Knowledge Base, chronic disease, elderlyAbstract
Most elderly people have increasingly suffered from chronic diseases. Artificial Intelligence--AI is often developed for treating the elderly’s health care in the backend tier in a form of classifying the results of being diseased and the risk of disease. This limitation leads to two proposed research objectives: (1) to develop the AI platform on the semantic knowledge base (AISKBP) for recommending the elderly with chronic disease care and (2) to assess the AISKB’s performance. The results of developing the AISKB can be classified into five parts: the user interfaces for inserting health values, the machine learning in classifying symptoms and diseases by training from the online health disease and stroke dataset, the semantic knowledge base with 33 nodes and 4 knowledge layers, the semantic rules with 6 parts, and displaying the elderly health categorization recommendation according to the disease and symptom classification results. AISKBP was developed using machine learning techniques for building disease classification models and connecting with the extracted ontology and semantic knowledge base from specialist doctors. It can provide automatic health care recommendations based on the elderly’s health states measured from IoT health sensors, medical Bluetooth devices, and user input via user interfaces. AISKBP can classify symptoms and diseases with a decision tree algorithm that has a maximum average accuracy of 100 percent. The health care recommendation with semantic knowledge base has correct and comprehensive results with an F-measure of 92.3%.
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