Prediction of burn wound response to low level laser using artificial intelligence

Ahmed Mohamed Nagy; Ayat Mahmoud Ahmed; Shaimaa Mohamed Metawee

doi:10.53730/ijhs.v6nS9.14714

Authors

Ahmed Mohamed Nagy Lecturer of Physical therapy for surgery and burn, faculty of physical therapy, Cairo University, Cairo, Egypt & Lecturer of Physical therapy for surgery and burn, faculty of physical therapy, October university for modern sciences and arts (MSA), Cairo, Egypt
Ayat Mahmoud Ahmed Lecturer of Computer Science, October University for Modern Science and Arts
Shaimaa Mohamed Metawee Lecturer of Basic sciences, faculty of physical therapy, Benha national University, Egypt

Keywords:

artificial intelligence, burn, decision tree, low level laser, ulcer, wound

Abstract

Background: chronic diseases increased with aging process, chronic wounds also increased, creating a huge load on the health system. Accurate documentation and measurement of wounds healing become critical. More research is required to construct and validate wound predictive measures to guide more accurate treatment plan with better clinical treatment. Artificial intelligence is widely used to help clinician in clinical decisions and save effort and time. A non-parametric supervised learning technique for regression and classification is called a decision tree (DT). The objective is to build a model that, by utilizing basic decision rules deduced from the data features, predicts the value of a target variable. A piecewise constant approximation can be thought of as a tree. Aim: to predict burn wound response to low level laser using artificial intelligence inform of decision tree tool through using the following variables: patients’ age, burn wound size ,wound stage and total burned surface area. Methods: fifty patients (male and female) with partial thickness burn wound were recruited from the burn units. There was only one intervention group receiving low level laser for six weeks divided to 18 sessions (three sessions per week).

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