Predictive classification of nosocomial infection type and treatment outcome using neural network algorithm

Therefore, the present study was designed to predict the type and outcome of treatment of nosocomial infection patients using neural network algorithm. The study is a cross-sectional one based on the registered data done on the nosocomial infection data of selected hospitals of Hamadan Province from March 2017 to March 2019, including 5,680 cases of nosocomial infections. According to patients' information registered in the Nosocomial Infection Registration System, nine criteria including the patients’ basic clinical information including name of the hospital and ward, infection-causing tool, organism, time from hospitalization to infection, length of hospital stay, age, gender, and weight were used as the explanatory criteria, and two criteria of type of infection and the final outcome of treatment, together with the actual available results, were used as the intended targets for performing the necessary analyses in data mining. In this study, neural network algorithm was implemented using IBM SPSS Modeler Software. The results showed that length of hospital stay and hospitalization ward were the most influencing factors on the incidence of nosocomial infection. This network is multilayer and has two output layers and 11 hidden layers as well as 9 input layers. The results of the algorithm, with an accuracy of 91.8%, have had good adjustment with the actual results. The results obtained from the model can help take an accurate step towards prediction of the likelihood of nosocomial infection and its outcome. It also identifies and prioritizes the factors influencing the incidence of infection.