A predictive healthcare model using machine learning and psychological factors for medication adherence
Ensuring effective medication adherence is vital for managing chronic diseases, yet global patient adherence remains suboptimal. This study aims to develop a predictive model for medication adherence behaviour (MAB) employing machine learning techniques, addressing the limitations of traditional cor...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Healthcare Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442525000164 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849689445584863232 |
|---|---|
| author | Junwu Dong Minyi Chu Yirou Xu |
| author_facet | Junwu Dong Minyi Chu Yirou Xu |
| author_sort | Junwu Dong |
| collection | DOAJ |
| description | Ensuring effective medication adherence is vital for managing chronic diseases, yet global patient adherence remains suboptimal. This study aims to develop a predictive model for medication adherence behaviour (MAB) employing machine learning techniques, addressing the limitations of traditional correlation-based approaches. Based on the Meta-Theoretic Model of Motivation and Personality (3M Model), data from 428 chronic disease patients, included dark triad traits (narcissism, Machiavellianism, psychopathy), general self-efficacy, doctor-patient trust, and demographic variables. Five machine learning algorithms – multiple logistic regression, decision tree, adaptive boosting, random forest and support vector machine (SVM) – were utilized to identify MAB levels and assess feature importance. Among these, the random forest model achieved the highest performance, with an accuracy of 0.637, recall of 0.538, precision of 0.556, and an F1 score of 0.544. Feature ranking revealed that narcissism, Machiavellianism, doctor-patient trust, psychopathy, and general self-efficacy were the most influential predictors. These findings demonstrate that integrating psychological and demographic factors into machine learning models can enhance the prediction of medication adherence. This study presents a novel interdisciplinary framework that integrates behavioural health analytics and data science to inform clinical decision-making. It provides valuable insights into the severity and temporal progression of medication adherence behaviours, offering clinicians a practical reference for developing more effective intervention strategies. |
| format | Article |
| id | doaj-art-c86ea0e9de6f4c1294a8b09bf72cc53f |
| institution | DOAJ |
| issn | 2772-4425 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj-art-c86ea0e9de6f4c1294a8b09bf72cc53f2025-08-20T03:21:38ZengElsevierHealthcare Analytics2772-44252025-06-01710039710.1016/j.health.2025.100397A predictive healthcare model using machine learning and psychological factors for medication adherenceJunwu Dong0Minyi Chu1Yirou Xu2School of Business, Guangdong University of Foreign Studies, Guangzhou, Guangdong Province, ChinaSchool of Public Administration, Guangdong University of Foreign Studies, GuangZhou, Guangdong Province, China; Corresponding author.School of Public Administration, Guangdong University of Foreign Studies, GuangZhou, Guangdong Province, ChinaEnsuring effective medication adherence is vital for managing chronic diseases, yet global patient adherence remains suboptimal. This study aims to develop a predictive model for medication adherence behaviour (MAB) employing machine learning techniques, addressing the limitations of traditional correlation-based approaches. Based on the Meta-Theoretic Model of Motivation and Personality (3M Model), data from 428 chronic disease patients, included dark triad traits (narcissism, Machiavellianism, psychopathy), general self-efficacy, doctor-patient trust, and demographic variables. Five machine learning algorithms – multiple logistic regression, decision tree, adaptive boosting, random forest and support vector machine (SVM) – were utilized to identify MAB levels and assess feature importance. Among these, the random forest model achieved the highest performance, with an accuracy of 0.637, recall of 0.538, precision of 0.556, and an F1 score of 0.544. Feature ranking revealed that narcissism, Machiavellianism, doctor-patient trust, psychopathy, and general self-efficacy were the most influential predictors. These findings demonstrate that integrating psychological and demographic factors into machine learning models can enhance the prediction of medication adherence. This study presents a novel interdisciplinary framework that integrates behavioural health analytics and data science to inform clinical decision-making. It provides valuable insights into the severity and temporal progression of medication adherence behaviours, offering clinicians a practical reference for developing more effective intervention strategies.http://www.sciencedirect.com/science/article/pii/S2772442525000164Machine learningPredictive modellingClinical decision-makingPsychological factorsMedication adherence analyticsChronic disease |
| spellingShingle | Junwu Dong Minyi Chu Yirou Xu A predictive healthcare model using machine learning and psychological factors for medication adherence Healthcare Analytics Machine learning Predictive modelling Clinical decision-making Psychological factors Medication adherence analytics Chronic disease |
| title | A predictive healthcare model using machine learning and psychological factors for medication adherence |
| title_full | A predictive healthcare model using machine learning and psychological factors for medication adherence |
| title_fullStr | A predictive healthcare model using machine learning and psychological factors for medication adherence |
| title_full_unstemmed | A predictive healthcare model using machine learning and psychological factors for medication adherence |
| title_short | A predictive healthcare model using machine learning and psychological factors for medication adherence |
| title_sort | predictive healthcare model using machine learning and psychological factors for medication adherence |
| topic | Machine learning Predictive modelling Clinical decision-making Psychological factors Medication adherence analytics Chronic disease |
| url | http://www.sciencedirect.com/science/article/pii/S2772442525000164 |
| work_keys_str_mv | AT junwudong apredictivehealthcaremodelusingmachinelearningandpsychologicalfactorsformedicationadherence AT minyichu apredictivehealthcaremodelusingmachinelearningandpsychologicalfactorsformedicationadherence AT yirouxu apredictivehealthcaremodelusingmachinelearningandpsychologicalfactorsformedicationadherence AT junwudong predictivehealthcaremodelusingmachinelearningandpsychologicalfactorsformedicationadherence AT minyichu predictivehealthcaremodelusingmachinelearningandpsychologicalfactorsformedicationadherence AT yirouxu predictivehealthcaremodelusingmachinelearningandpsychologicalfactorsformedicationadherence |