Applications where data mining tools are used in the fields of medicine and nursing are becoming more and more frequent. Among them, decision trees have been applied to different health data, such as those associated with pressure ulcers. Pressure ulcers represent a health problem with a significant impact on the morbidity and mortality of immobilized patients and on the quality of life of affected people and their families. Nurses provide comprehensive care to immobilized patients. This fact results in an increased workload that can be a risk factor for the development of serious health problems. Healthcare work with evidence-based practice with an objective criterion for a nursing professional is an essential addition for the application of preventive measures. In this work, two ways for conducting a pressure ulcer risk assessment based on a decision tree approach are provided. The first way is based on the activity and mobility characteristics of the Braden scale, whilst the second way is based on the activity, mobility and skin moisture characteristics. The results provided in this study endow nursing professionals with a foundation in relation to the use of their experience and objective criteria for quick decision making regarding the risk of a patient to develop a pressure ulcer.

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Pressure ulcers (PU) represent a health problem with a significant impact on the morbidity and mortality of immobilized patients, and on the quality of life of affected people and their families. Risk assessment of pressure ulcers incidence must be carried out in a structured and comprehensive manner. The Braden Scale is the result of an analysis of risk factors that includes subscales that define exactly what should be interpreted in each one. The healthcare work with evidence-based practice with an objective criterion by the nursing professional is an essential addition for the application of preventive measures. Explanatory models based on the different subscales of Braden Scale purvey an estimation to level changes in the risk of suffering PU. A binary-response logistic regression model, supported by a study with an analytical, observational, longitudinal, and prospective design in the Granada-Metropolitan Primary Healthcare District (DSGM) in Andalusia (Southern Spain), with a sample of 16,215 immobilized status patients, using a Braden Scale log, is performed. A model that includes the mobility and activity scales achieves a correct classification rate of 86% (sensitivity (S) = 87.57%, specificity (SP) = 81.69%, positive predictive value (PPV) = 91.78%, and negative preventive value (NPV) = 73.78%), while if we add the skin moisture subscale to this model, the correct classification rate is 96% (S = 90.74%, SP = 88.83%, PPV = 95.00%, and NPV = 80.42%). The six subscales provide a model with a 99.5% correct classification rate (S = 99.93%, SP = 98.50%, PPV = 99.36%, and NPV = 99.83%). This analysis provides useful information to help predict this risk in this group of patients through objective nursing criteria.