A Voice-based Triage for Type 2 Diabetes using a Conversational Virtual Assistant in the Home Environment

TL;DR

This paper presents a novel voice-based triaging system using acoustic machine learning to pre-screen for Type 2 Diabetes in older adults via a conversational virtual assistant, achieving promising accuracy and operating on resource-limited devices.

Contribution

It introduces a new voice analysis approach integrated into virtual assistants for early diabetes detection, utilizing non-identifiable acoustic features suitable for embedded systems.

Findings

Achieved 70% and 60% hit-rates for male and female older adults.

Uses only 7 non-identifiable voice features.

Demonstrates feasibility of home-based early detection of diabetes.

Abstract

Incorporating cloud technology with Internet of Medical Things for ubiquitous healthcare has seen many successful applications in the last decade with the advent of machine learning and deep learning techniques. One of these applications, namely voice-based pathology, has yet to receive notable attention from academia and industry. Applying voice analysis to early detection of fatal diseases holds much promise to improve health outcomes and quality of life of patients. In this paper, we propose a novel application of acoustic machine learning based triaging into commoditised conversational virtual assistant systems to pre-screen for onset of diabetes. Specifically, we developed a triaging system which extracts acoustic features from the voices of n=24 older adults when they converse with a virtual assistant and predict the incidence of Diabetes Mellitus (Type 2) or not. Our triaging system achieved hit-rates of 70% and 60% for male and female older adult subjects, respectively. Our proposed triaging uses 7 non-identifiable voice-based features and can operate within resource-constrained embedded systems running voice-based virtual assistants. This application demonstrates the feasibility of applying voice-based pathology analysis to improve health outcomes of older adults within the home environment by early detection of life-changing chronic conditions like diabetes.