Visualisation of Survey Responses using Self-Organising Maps: A Case Study on Diabetes Self-care Factors

TL;DR

This paper demonstrates how self-organising maps can be used to visualize survey responses from diabetes patients, revealing behavioral patterns that can inform treatment strategies and improve patient management.

Contribution

It introduces a novel application of self-organising maps for visualizing and analyzing survey data in diabetes self-care, aiding clinicians in understanding patient behaviors.

Findings

Identified key behavioral patterns in diabetes self-care responses.

Showed correlations between medication timing and dosage management.

Provided insights for targeted intervention strategies.

Abstract

Due to the chronic nature of diabetes, patient self-care factors play an important role in any treatment plan. In order to understand the behaviour of patients in response to medical advice on self-care, clinicians often conduct cross-sectional surveys. When analysing the survey data, statistical machine learning methods can potentially provide additional insight into the data either through deeper understanding of the patterns present or making information available to clinicians in an intuitive manner. In this study, we use self-organising maps (SOMs) to visualise the responses of patients who share similar responses to survey questions, with the goal of helping clinicians understand how patients are managing their treatment and where action should be taken. The principle behavioural patterns revealed through this are that: patients who take the correct dose of insulin also tend to take their injections at the correct time, patients who eat on time also tend to correctly manage their food portions and patients who check their blood glucose with a monitor also tend to adjust their insulin dosage and carry snacks to counter low blood glucose. The identification of these positive behavioural patterns can also help to inform treatment by exploiting their negative corollaries.