Predicting Progression of Type 2 Diabetes Using Primary Care Data with the Help of Machine Learning

TY - CHAP

T1 - Predicting Progression of Type 2 Diabetes Using Primary Care Data with the Help of Machine Learning

AU - Ozturk, Berk

AU - Lawton, Tom

AU - Smith, Stephen

AU - Habli, Ibrahim

N1 - © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.

PY - 2023/5/18

Y1 - 2023/5/18

N2 - Type 2 diabetes is a life-long health condition, and as it progresses, A range of comorbidities can develop. The prevalence of diabetes has increased gradually, and it is expected that 642 million adults will be living with diabetes by 2040. Early and proper interventions for managing diabetes-related comorbidities are important. In this study, we propose a Machine Learning (ML) model for predicting the risk of developing hypertension for patients who already have Type 2 diabetes. We used the Connected Bradford dataset, consisting of 1.4 million patients, as our main dataset for data analysis and model building. As a result of data analysis, we found that hypertension is the most frequent observation among patients having Type 2 diabetes. Since hypertension is very important to predict clinically poor outcomes such as risk of heart, brain, kidney, and other diseases, it is crucial to make early and accurate predictions of the risk of having hypertension for Type 2 diabetic patients. We used Naïve Bayes (NB), Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM) to train our model. Then we ensembled these models to see the potential performance improvement. The ensemble method gave the best classification performance values of accuracy and kappa values of 0.9525 and 0.2183, respectively. We concluded that predicting the risk of developing hypertension for Type 2 diabetic patients using ML provides a promising stepping stone for preventing the Type 2 diabetes progression.

AB - Type 2 diabetes is a life-long health condition, and as it progresses, A range of comorbidities can develop. The prevalence of diabetes has increased gradually, and it is expected that 642 million adults will be living with diabetes by 2040. Early and proper interventions for managing diabetes-related comorbidities are important. In this study, we propose a Machine Learning (ML) model for predicting the risk of developing hypertension for patients who already have Type 2 diabetes. We used the Connected Bradford dataset, consisting of 1.4 million patients, as our main dataset for data analysis and model building. As a result of data analysis, we found that hypertension is the most frequent observation among patients having Type 2 diabetes. Since hypertension is very important to predict clinically poor outcomes such as risk of heart, brain, kidney, and other diseases, it is crucial to make early and accurate predictions of the risk of having hypertension for Type 2 diabetic patients. We used Naïve Bayes (NB), Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM) to train our model. Then we ensembled these models to see the potential performance improvement. The ensemble method gave the best classification performance values of accuracy and kappa values of 0.9525 and 0.2183, respectively. We concluded that predicting the risk of developing hypertension for Type 2 diabetic patients using ML provides a promising stepping stone for preventing the Type 2 diabetes progression.

KW - Machine learning

KW - AI

KW - Diabetes Mellitus, Type 2/diagnosis

KW - Healthcare

KW - Machine Learning

KW - Hypertension/diagnosis

KW - Primary Health Care

KW - Support Vector Machine

U2 - 10.3233/SHTI230060

DO - 10.3233/SHTI230060

M3 - Chapter

C2 - 37203605

SN - 978-1-64368-388-1

VL - 302

T3 - Studies in health technology and informatics

SP - 38

EP - 42

BT - Caring is Sharing

ER -