Predictive models of medication non-adherence risks of patients with T2D based on multiple machine learning algorithms

Abstract

Objective: 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.

Methods: A real-world registration study was conducted at Sichuan Provincial People's Hospital from 1 April 2018 to 30 March 2019. Data of patients with T2D 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, and so on, and balanced sampling, data imputation, binning, and methods of feature selection were evaluated by the area under the 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.

Results: A total of 401 patients out of 630 candidates were investigated, of which 85 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, oversampling and larger sample size will help improve predictive ability.

Conclusions: An accurate and sensitive adherence prediction model based on real-world registration data was established after evaluating data filling, balanced sampling, and so on, which may provide a technical tool for individualized diabetes care.

Keywords: adherence; personality; prediction and prevention; type 2 diabetes.

Figure 1

The data flowed into the ‘Partition’ node after feature selection. The ‘Auto Data Prep’ node was used for data filling, the ‘Balance’ node performed a data balanced sampling process, and the ‘Binning’ node was applied for data binning. The ‘Partition’ node divided set 1 into a training set and a testing set, used the ‘Auto Classifier’ node to build various classification models, and used the ‘Analysis’ and ‘Evaluation’ nodes to output the AUC values and curve figure of each model. Use the ‘Select’ node to select the ‘Set 2’ data set. The set 2 set was concatenated with the models established above and the ensemble model of them, and the AUC values and graphs of all models were output using the ‘Analysis’ and ‘Evaluation’ nodes. AUC, area under the receiver operating characteristic curve.