A Collaborative Filtering Recommender System Model for Recommending Intervention to Improve Elderly Well-being

Aini Khairani Azmi,Noraswaliza Abdullah,Nurul Akmar

doi:10.14569/ijacsa.2019.0100619

Abstract

In improving elderly well-being nowadays, people at home or health care centre are mostly focusing on guarding and monitoring the elderly using tools, such as CCTV, robots, and other appliances that require a great deal of cost and neat fixtures to prevent damage. Elderly observations using the recommender system are found to be implemented, but only focusing on one aspect such as nutrition and health. However, it is important to give interventions to an elderly by concentrating more on the multiple aspects of successful ageing such as social, environment, health, physical, mental and other so that it can help the elderly people in achieving successful ageing as well as improving their well-being. In this paper, two recommender system models are proposed to recommend interventions for improving elderly well-being in the multiple aspects of successful ageing. These models using a Collaborative Filtering (CF) technique to recommend interventions to an elderly based on the interventions given to other elderly who have similar conditions with the user. The process of recommending interventions involves the generation of user profiles presenting the elderly conditions in multiple aspects of successful ageing. It also applying the k-Nearest Neighbor (kNN) method to find users with similar conditions and recommending interventions based on the interventions given to the similar user. The experiment is conducted to determine the performance of the proposed Collaborative Filtering (CF) recommender system and Collaborative Filtering and Profile Matching (CFS) compared to the Basic Search (BS). The results of the experiment showed that Collaborative Filtering (CF) recommender system and Collaborative Filtering and Profile Matching (CFS) outperformed Basic Search (BS) in terms of precision, recall and F1 measure. This result showed that the proposed models are efficient to recommend interventions using elderly profiles based on many aspects of successful ageing.

Highlights

The significant increase in life expectancy at birth has been achieved over the last century
The objective of this set of experiments was to verify that the result of interventions by using Collaborative Filtering with Profile Matching (CFS) approaches can generate more accurate recommendations compared to Basic Search (BS) approaches and CF
The highest precision of CF shows that the interventions recommended by CF approach are accurate and suitable according to the new elderly profile or condition

Summary

Introduction

The significant increase in life expectancy at birth has been achieved over the last century. A combination of medical advances, escalating health and social care costs, and higher expectations for older age have brought international interest in promoting healthier elderly and achieving "successful" ageing. According to [1], successful ageing includes three main components which are; 1) low probability of disease and disease-related disability, 2) high cognitive and physical functional capacity, and 3) active engagement in social and productive activities. Nutrition and environmental factors are needed for elderly to age well, as well as achieving successful ageing [4][6]. It can be concluded that in order to achieve successful ageing, there are many aspects that need to be focused on for improving elderly well-being such as socialization, health, cognitive, physical, environment, nutrition and spirituality

Objectives

Results

Conclusion