Use of machine learning to predict medication adherence in individuals at risk for atherosclerotic cardiovascular disease

Seyed Iman Mirzadeh, Asiful Arefeen, Jessica Ardo, Ramin Fallahzadeh, Bryan Minor, Jung-Ah Lee, Janett A Hildebrand, Diane Cook, Hassan Ghasemzadeh, Lorraine S Evangelista, Seyed Iman Mirzadeh, Asiful Arefeen, Jessica Ardo, Ramin Fallahzadeh, Bryan Minor, Jung-Ah Lee, Janett A Hildebrand, Diane Cook, Hassan Ghasemzadeh, Lorraine S Evangelista

Abstract

Background: Medication nonadherence is a critical problem with severe implications in individuals at risk for atherosclerotic cardiovascular disease. Many studies have attempted to predict medication adherence in this population, but few, if any, have been effective in prediction, sug-gesting that essential risk factors remain unidentified.

Objective: This study's objective was to (1) establish an accurate prediction model of medi-cation adherence in individuals at risk for atherosclerotic cardiovascular disease and (2) identify significant contributing factors to the predictive accuracy of medication adherence. In particular, we aimed to use only the baseline questionnaire data to assess medication adherence prediction feasibility.

Methods: A sample of 40 individuals at risk for atherosclerotic cardiovascular disease was recruited for an eight-week feasibility study. After collecting baseline data, we recorded data from a pillbox that sent events to a cloud-based server. Health measures and medication use events were analyzed using machine learning algorithms to identify variables that best predict medication adherence.

Results: Our adherence prediction model, based on only the ten most relevant variables, achieved an average error rate of 12.9%. Medication adherence was closely correlated with being encouraged to play an active role in their treatment, having confidence about what to do in an emergency, knowledge about their medications, and having a special person in their life.

Conclusions: Our results showed the significance of clinical and psychosocial factors for predicting medication adherence in people at risk for atherosclerotic cardiovascular diseases. Clini-cians and researchers can use these factors to stratify individuals to make evidence-based decisions to reduce the risks.

Keywords: Medication adherence machine learning cardiovascular diseases cloud computing Surveys and questionnaires.

Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1.
Fig. 1.
Machine learning pipeline for adherence prediction. Based on the requirements, we applied three types of feature extraction: i. keep one, leave rest was employed to study the individual effect of each questionnaire, ii. RFE and F values based rankings were done to identify the relative contribution of the features among all.

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