METHODS: A prospective, randomized, single-blinded control trial was performed on eligible diabetic patients with full-thickness cavity wounds. Patients' demographics, size and site of wounds, and baseline routine blood investigations were recorded. The wounds were dressed every other day with Kelulut honey for the intervention group or gel for the control group. The wound size reduction and granulation tissue formation percentage were calculated every 6 days for 1 month.
RESULTS: Seventy-one patients were randomized. After 30 days of follow-up, 62 participants were available for analysis: 30 from the control group and 32 from the treatment group. The control group had increased granulation tissue at baseline and more wounds on the lower limb and posterior trunk. Both groups showed an increasing mean and median percentage of wound epithelialization and granulation tissue over time, with significantly higher values at every timepoint in the honey group (p
RESULTS: Prediction in two and three state classification systems with several thresholds are provided. Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. Three state prediction results provide a maximum 65% accuracy for training and 63% for the test data. Applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds. Salient differences between a linear and exponential network for ASA prediction have been analysed.
AVAILABILITY: Online predictions are freely available at: http://www.netasa.org. Linux ix86 binaries of the program written for this work may be obtained by email from the corresponding author.