Abstract
Diabetes mellitus (DM) and essential hypertension (EH) are chronic diseases more prevalent every year, both independently and jointly. To gain insights about the particularities of these chronic conditions, we study the use of decision trees as a tool for selecting discriminative features and making predictive analyses of the health status of this kind of chronic patients. We considered gender, age, ICD9 codes for diagnosis and ATC codes for drugs associated with the diabetic and/or hypertensive population linked to the University Hospital of Fuenlabrada (Madrid, Spain) during 2012. Results show a relationship among DM/EH and diseases/drugs related to the respiratory system, mental disorders, or the musculoskeletal system. We conclude that drugs are quite informative, collecting information about the disease when the diagnosis code is not registered. Regarding predictive analyses, when discriminating patients with EH-DM and just one of these chronic conditions, better accuracy is obtained for EH (85.4%) versus DM (80.1%).
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Acknowledgments
This work has been partly supported by Research Projects TEC2016-75361-R, TIN2014-62143-EXP, and TIN2015-70799-R from the Spanish Government, and Research Project DTS17/00158 from Carlos III Institute.
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Soguero-Ruiz, C. et al. (2018). On the Use of Decision Trees Based on Diagnosis and Drug Codes for Analyzing Chronic Patients. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10814. Springer, Cham. https://doi.org/10.1007/978-3-319-78759-6_14
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DOI: https://doi.org/10.1007/978-3-319-78759-6_14
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