Hospitalization status and gender recognition over the arboviral medical records using shallow and RNN-based deep models

In global health systems, clinicians have a challenging decision of a triage patient exposed to arbovirus infections to determine they should be hospitalized. Diagnosing symptoms and molecular testing can be uncertain and costly, especially in resource-limited settings. However, machine learning approaches have a high potential to determine through medical record examination whom to hospitalize. The purpose of this study is to determine hospitalized or outpatient individuals correctly by implementing shallow machine learning algorithms on SISA (Severity Index for Suspected Arbovirus) and SISAL (Severity Index for Suspected Arbovirus with Laboratory) datasets. Feed Forward Neural Network (FFNN), Probabilistic Neural Network (PNN), and Decision Tree (DecT) algorithm with three splitting criterions were used to process the SISA and SISAL datasets. The results of clas-sification performances demonstrated that improved area under the curve scores (0.973) and accuracy (reaching up to 98.73% with FFNN) were obtained when compared with previous research study related to machine learning and arbovirus. Moreover, this study also aims to investigate gender recognition over arboviral infection medical records using recurrent neural network-based deep models. Hence vector control policy in the health system reflects the gender roles to control the spread of arboviral infection. Overall the outcomes are potentially very promising.