The model is designed to predict whether a patient has Covid-19 or influenza based on clinical data. Testing patient rapidly for Covid-19 is a key step in tackling the rapidly spreading pandemic, hence this machine learning model could help to identify Covid-19 patients. Patients of Covid-19 and influenza show very similar symptoms so its difficult to distinguish them, so this model is trained on clinical data for both patients to distinguish them well. Variables like patient demographics, symptoms and results of various tests were considered for building the model. A clustering technique is used to gain insights from data and Extreme Gradient Boosting (XGBoost) is applied to predict the Covid-19 patient.
Input variables : Patient demographics, Clinical variables, Reported symptoms
Output Variables : Prediction of Covid-19 infection
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : bmcmedinformdecismak.biomedcentral.com
Additional links : bmcmedinformdecismak.biomedcentral.com
| Model Category | : | Public |
| Date Published | : | September, 2020 |
| Healthcare Domain | : | Provider |
| Code | : | github.com |
| Health Risk Management |
| Disease Detection |