Determining hypertensive patients’ beliefs towards medication and associations with medication adherence using machine learning methods

PeerJ

article

Mogeeb Mosleh¹  Pozi Milow²  Firdaus Aziz³  Sorayya Malek³  Adliah Mhd Ali⁴  Mee Sieng Wong⁴ 

[1] Software Engineering Department, Faculty of Engineering & Information Technology, Taiz University;Environmental Management Programme, Institute of Biological Sciences, University of Malaya;Bioinformatics Science Programme, Institute of Biological Sciences, University of Malaya;Quality Use of Medicines Research Group, Faculty of Pharmacy, Universiti Kebangsaan Malaysia

关键词: Random forest;    Artificial neural network;    Self-organizing Map (SOM);    Hypertension;    Support Vector Regression;    Variable importance;    Adherence level;   

DOI  :  10.7717/peerj.8286

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundThis study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients’ adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients’ adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature.MethodsData from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients’ adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients’ adherence levels and variables were generated using SOM.ResultMachine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.ConclusionThis study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients’ adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.

【 授权许可】

CC BY