Abstract
Medication adherence plays a key role in type 2 diabetes(T2D) care. Identifying patients with high risks of non-compliance helps individualized management, especially for China, where medical resources are relatively insufficient. However, models with good predictive capabilities have not been studied. This study aims to assess multiple machine learning algorithms and screen out a model that can be used to predict patients’ non-adherence risks. A real-world registration study was conducted at Sichuan Provincial People’s Hospital from 1 April 2018 to 30 March 2019. T2D patients’ data on demographics, disease and treatment, diet and exercise, mental status, and treatment adherence were obtained by face-to-face questionnaires. The medication possession ratio was used to evaluate patients’ medication adherence status. Fourteen machine learning algorithms were applied for modeling, including Bayesian network, Neural Net, Support vector machine, etc., and balance-sampling, data imputation, binning, methods of feature selection were evaluated by the Area under Receiver Operating Characteristic curve (AUC). We use two-way cross-validation to ensure the accuracy of model evaluation. And we performed a posteriori test on the sample size based on the trend of AUC as the sample size increase. A total of 401 patients out of 630 candidates were investigated, of which 85 patients were evaluated as poor adherence (21.20%). A total of 16 variables were selected as potential variables for modeling, and 300 models were built based on 30 machine learning algorithms. Among these algorithms, the AUC of the best capable one was 0.866±0.082. Imputing, over-sampling and larger sample size will help to improve predictive ability. An accurate and sensitive adherence prediction model based on real-world registration data was established after evaluating data filling and balanced sampling etc., which may provide a technical tool for individualized diabetes care.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
