Application of artificial intelligence in the Prediction of Complications in patients with Malaria

Introduction: This study aims to develop a neural network (NN) that can serve as a useful tool for early diagnosis of complicated malaria. Materials and methods: In this study, a feedforward NN was developed, incorporating 10 clinical variables in the input nodes, hidden layer, and output node. The data were included in the input layer. Various validation techniques such as V cross, Random V cross, Modified Holdout, and Proportional Percentage Sample were applied to train and validate the network using data from 412 patients. Results: The variables included in the analysis were mean arterial pressure, hemoglobin, leukocyte count, platelet count, total bilirubin, presence of dyspnea, vomiting, previous history of malaria, prior use of malaria medication, and persistent fever. The V cross technique, Random V cross Validation, Modified Holdout Validation, and Proportional Percentage Sample Validation were utilized to evaluate the performance of a NN in diagnosing malaria. Sensitivity values varied from 13% to 47%, with positive predictive value values ranging from 37% to 88%. Specificity remained consistently high, ranging from 79% to 90%. Discussion: Sensitivity, specificity, and positive predictive values varied across techniques: V cross and random V cross validation showed narrower sensitivity ranges with strong specificities, while modified holdout validation exhibited wider sensitivity variability.