HealthAdvisor: Recommendation System for Wearable Technologies enabling Proactive Health Monitoring

TL;DR

HealthAdvisor is a personalized recommendation system that uses machine learning and textual analytics to suggest wearable devices for proactive health monitoring based on individual health risks and needs.

Contribution

It introduces a novel integrated approach combining disease risk prediction and wearable mapping for personalized health device recommendations.

Findings

Effective identification of health risks using ML models

Successful mapping of health measurements to wearable devices

Provides actionable feedback to wearable developers

Abstract

Proactive monitoring of one's health could avoid serious diseases as well as better maintain the individual's well-being. In today's IoT world, there has been numerous wearable technological devices to monitor/measure different health attributes. However, with that increasing number of attributes and wearables, it becomes unclear to the individual which ones they should be using. The aim of this paper is to provide a recommendation engine for personalized recommended wearables for any given individual. The way the engine works is through first identifying the diseases that this person is at risk of, given his/her attributes and medical history. We built a machine learning classification model for this task. Second, these diseases are mapped to the attributes that need to be measured in order to monitor such diseases. Third, we map these measurements to the appropriate wearable technologies. This is done via a textual analytics model that we developed that uses available information of different wearables to map the aforementioned measurements to these wearables. The output can be used to recommend the wearables to individuals as well as provide a feedback to wearable developers for common measurements that do not have corresponding wearables today.