Evaluation of Prognostic Factors and Prediction of Chronic Wound Healing Rate by Machine Learning Tools

In more than a decade of clinical use of electrical stimulation to accelerate the chronic wound healing each patient and wound were registered and a wound healing process was weekly followed. The controlled study involved a conventional conservative treatment, sham treatment, biphasic pulsed current, and direct current electrical stimulation. A quantity of available data suffices for an analysis with machine learning methods. So far only a limited number of studies have investigated the wound and patient attributes which affect the chronic wound healing. There is none to our knowledge to include the treatment attributes. The aims of our study are to determine effects of the wound, patient and treatment attributes on the wound healing process and to propose a system for prediction of the wound healing rate. In the first step of our analysis we determined which wound and patient attributes play a predominant role in the wound healing process. Then we investigated a possibility to predict the wound healing rate at the beginning of the treatment based on the initial wound, patient and treatment attributes. Finally we discussed the possibility to enhance the wound healing rate prediction accuracy by predicting it after a few weeks of the wound healing follow-up. By using the attribute estimation algorithms ReliefF and RReliefF we obtained a ranking of the prognostic factors which was comprehensible to field experts. We also used regression and classification trees to build models for prediction of the wound healing rate. The obtained results are encouraging and may form a basis of an expert system for the chronic wound healing rate prediction. If the wound healing rate is known, then the provided information can help to formulate the appropriate treatment decisions and orient resources to those individuals with