An Artificial Neural Network based Model to Analyze Malarial Data and Predict Organ Failure Abstract

Health Care Management is one of the most important and most important research areas of the new millennium. The main purpose of this work was to analyze the data on malaria patients in India using the artificial neural networks such as Brainmaker and statistical analysis software (SAS). This data is known that SOFA (Sequential Organ Failure Assessment) score and this information useful in providing the condition of the organ and based on this, the patient’s survival rate can be estimated. In this analysis, the same individual SOFA score of different organ systems (esp. the ones which are used to calculate the overall SOFA score) for 753 patients admitted in an hospital in India with malaria were trained using artificial neural networks to provide better predictions of the survival rate compared to the overall SOFA scores. Using the statistical analysis tools like SAS, the statistical aspects of this data was studied. Also, using SAS, analysis was done on the data of the Indian malaria patients and the interested outcomes were projected in the figures at the end of this paper. The results show that the artificial neural network turns out to be an efficient predictor of survival rate and the results were comparable to the SOFA scoring system where the same variables used for calculating the overall SOFA score were used for training the neural network