An Intelligent Long-Term Care Information Platform using Machine Learning and Semantic Web

Abstract

Abstract The low use of Long-Term Care (LTC) services in Taiwan is mainly caused by the lack of convenient promotion tools. This study developed a general architecture integrating Clustering Algorithm into Domain Ontology based on Cloud Computing (CADOCC) to provide a cloud computing environment and support the big data required for machine learning. The proposed CADOCC comprised four integrated components: cloud computing, machine learning, data preparation, and semantic web modules. The proposed CADOCC was used to develop an LTC Application Platform (LTCAP) to assist users in automatically classifying and filtering articles related to LTC. Additionally, the LTCAP was used to produce an intelligent mobile app that continually learns from user question–answer interactions through machine learning to facilitate the use of LTC services for personal requirements. The performance and satisfaction scores of classification algorithms running in different spark cloud computing environments were tested and compared. The results revealed that LSI and K-means met requirements in a test scenario, and the total satisfaction score was 4.15, which confirmed the feasibility of CADOCC.