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Classification of patients with chronic disease by activation level using machine learning methods

Author

Listed:
  • Onur Demiray

    (Imperial College London)

  • Evrim D. Gunes

    (Koç University, Rumeli Feneri Yolu)

  • Ercan Kulak

    (Ministry of Health Caycuma District Health Directorate)

  • Emrah Dogan

    (Ministry of Health, Zonguldak Community Health Center)

  • Seyma Gorcin Karaketir

    (Department of Public Health, Istanbul University)

  • Serap Cifcili

    (Department of Family Medicine, Marmara University School of Medicine)

  • Mehmet Akman

    (Department of Family Medicine, Marmara University School of Medicine)

  • Sibel Sakarya

    (Koç University, Rumeli Feneri Yolu)

Abstract

Patient Activation Measure (PAM) measures the activation level of patients with chronic conditions and correlates well with patient adherence behavior, health outcomes, and healthcare costs. PAM is increasingly used in practice to identify patients needing more support from the care team. We define PAM levels 1 and 2 as low PAM and investigate the performance of eight machine learning methods (Logistic Regression, Lasso Regression, Ridge Regression, Random Forest, Gradient Boosted Trees, Support Vector Machines, Decision Trees, Neural Networks) to classify patients. Primary data collected from adult patients (n=431) with Diabetes Mellitus (DM) or Hypertension (HT) attending Family Health Centers in Istanbul, Turkey, is used to test the methods. $$44.5\%$$ 44.5 % of patients in the dataset have a low PAM level. Classification performance with several feature sets was analyzed to understand the relative importance of different types of information and provide insights. The most important features are found as whether the patient performs self-monitoring, smoking and exercise habits, education, and socio-economic status. The best performance was achieved with the Logistic Regression algorithm, with Area Under the Curve (AUC)=0.72 with the best performing feature set. Alternative feature sets with similar prediction performance are also presented. The prediction performance was inferior with an automated feature selection method, supporting the importance of using domain knowledge in machine learning.

Suggested Citation

  • Onur Demiray & Evrim D. Gunes & Ercan Kulak & Emrah Dogan & Seyma Gorcin Karaketir & Serap Cifcili & Mehmet Akman & Sibel Sakarya, 2023. "Classification of patients with chronic disease by activation level using machine learning methods," Health Care Management Science, Springer, vol. 26(4), pages 626-650, December.
  • Handle: RePEc:kap:hcarem:v:26:y:2023:i:4:d:10.1007_s10729-023-09653-4
    DOI: 10.1007/s10729-023-09653-4
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    References listed on IDEAS

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    1. Carrie Queenan & Kellas Cameron & Alan Snell & Julia Smalley & Nitin Joglekar, 2019. "Patient Heal Thyself: Reducing Hospital Readmissions with Technology‐Enabled Continuity of Care and Patient Activation," Production and Operations Management, Production and Operations Management Society, vol. 28(11), pages 2841-2853, November.
    2. Bulent Kilic & Sibel Kalaca & Belgin Unal & Peter Phillimore & Shahaduz Zaman, 2015. "Health policy analysis for prevention and control of cardiovascular diseases and diabetes mellitus in Turkey," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 60(1), pages 47-53, January.
    3. Wang, Haifeng & Zheng, Bichen & Yoon, Sang Won & Ko, Hoo Sang, 2018. "A support vector machine-based ensemble algorithm for breast cancer diagnosis," European Journal of Operational Research, Elsevier, vol. 267(2), pages 687-699.
    4. Samuel G Smith & Laura M Curtis & Jane Wardle & Christian von Wagner & Michael S Wolf, 2013. "Skill Set or Mind Set? Associations between Health Literacy, Patient Activation and Health," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-7, September.
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