Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction

Nikos Fazakis,Otilia Kocsis,Nikos Fakotakis,Sotiris Alexiou,Elias Dritsas,Konstantinos Moustakas

doi:10.1109/access.2021.3098691

Nikos Fazakis, Otilia Kocsis + Show 4 more

Open Access

https://doi.org/10.1109/access.2021.3098691

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Abstract

A steady rise has been observed in the percentage of elderly people who want and are still able to contribute to society. Therefore, early retirement or exit from the labour market, due to health-related issues, poses a significant problem. Nowadays, thanks to technological advances and various data from different populations, the risk factors investigation and health issues screening are moving towards automation. In the context of this work, a worker-centric, IoT enabled unobtrusive users health, well-being and functional ability monitoring framework, empowered with AI tools, is proposed. Diabetes is a high-prevalence chronic condition with harmful consequences for the quality of life and high mortality rate for people worldwide, in both developed and developing countries. Hence, its severe impact on humans' life, e.g., personal, social, working, can be considerably reduced if early detection is possible, but most research works in this field fail to provide a more personalized approach both in the modeling and prediction process. In this direction, our designed system concerns diabetes risk prediction in which specific components of the Knowledge Discovery in Database (KDD) process are applied, evaluated and incorporated. Specifically, dataset creation, features selection and classification, using different Supervised Machine Learning (ML) models are considered. The ensemble WeightedVotingLRRFs ML model is proposed to improve the prediction of diabetes, scoring an Area Under the ROC Curve (AUC) of 0.884. Concerning the weighted voting, the optimal weights are estimated by their corresponding Sensitivity and AUC of the ML model based on a bi-objective genetic algorithm. Also, a comparative study is presented among the Finnish Diabetes Risk Score (FINDRISC) and Leicester risk score systems and several ML models, using inductive and transductive learning. The experiments were conducted using data extracted from the English Longitudinal Study of Ageing (ELSA) database.

Highlights

Diabetes, known as diabetes mellitus (DM), is a chronic disorder characterized by high blood glucose levels, due to the inability of the pancreas to generate a sufficient quantity of insulin (Diabetes Mellitus Type-1 (T1DM)) or the failure of cells and tissues to utilize it (Diabetes Mellitus Type-2 (T2DM)) [1]
3) MACHINE LEARNING MODELS Let recall that, in the context of this work, we investigate the problem of T2DM prediction on English Longitudinal Study of Ageing (ELSA) database with various machine learning models
The selected features for the Machine Learning (ML) models training and testing are among the symptoms/factors that doctors consider for quantifying long-term risk prediction or identifying its occurrence

Summary

Introduction

Known as diabetes mellitus (DM), is a chronic disorder characterized by high blood glucose levels, due to the inability of the pancreas to generate a sufficient quantity of insulin (Diabetes Mellitus Type-1 (T1DM)) or the failure of cells and tissues to utilize it (Diabetes Mellitus Type-2 (T2DM)) [1]. Apart from T1DM and T2DM, another type is Gestational diabetes, which affects women and develops during pregnancy. Since the prevalence of T2DM in ageing population (i.e., elderly people) is rising [2], [3], the analysis. The prevention and/or early diagnosis of diabetes is of high importance in order to avoid or mitigate the serious lifetime complications including cardiovascular ailment, stroke, kidney failure, ulcers in the foot, and eye complications etc [5], [6].

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