Artificial intelligence in differentiating tropical infections: A step ahead - Shenoy, Shreelaxmi; Rajan, Asha K.; Rashid, Muhammed; Chandran, Viji Pulikkel; Poojari, Pooja Gopal; Kunhikatta, Vijayanarayana; Acharya, Dinesh; Nair, Sreedharan; Varma, Muralidhar; Thunga, Girish (PLOS Neglected Tropical Diseases, 2022)
Author summary Distinguishing tropical infections is difficult due to its homogeneous nature from clinical and laboratory presentations among them. This is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings as per the need analysis. Better predictors in terms of laboratory parameters and clinical presentations were identified from retrospective analysis and used for the regression and machine learning models. The parameters such as accuracy, true positive rate/sensitivity/recall, false positive rate, precision/positive predictive value, F-measure and ROC area for both the training and validation sets (10-fold cross validation) for all modelling approaches and diseases (One vs One and One vs others) were calculated. All the models observed to have an acceptable range of model performance in differentiating tropical infections. Albumin can be considered as the main parameter in differentiating these tropical infections. These models should be implemented in daily clinical routine practice via mobile or desktop assisted applications or tools.