The problem HPA solves

Early and effective prediction of the incidence trend of Hepatitis B can provide a scientific basis for the prevention and treatment of Hepatitis B, as well as for the rational allocation of health resources, thereby reducing unnecessary waste. However, existing office Hepatitis B testing organizations in almost every country only collect the number of suspected Hepatitis B cases in the hospital population to use it as the survey data on Hepatitis B incidence. This approach requires the establishment of a nationwide monitoring network, where the collection and processing of data have to go through complex processes. As a result, the monitoring data for lags behind the actual development of the disease . In light of this, there are direct prediction models of Hepatitis B incident trends based on time series, combined model, and the gray model. These studies help to improve the accuracy and timeliness of short-term prediction mechanisms.
At present, researchers at home and abroad are actively using a variety of methods to come up with different prediction schemes, but most of them are based on short-term data for correlation and extrapolate the future using historical data. Although such methods also have some practical significance, the resulting schemes tend to be untimely in their predictions, which are especially problematic for populations of infected persons that can change suddenly and dramatically within very short periods of time. In addition, the analysis of a single factor is not enough to be able to fully grasp the characteristics of epidemics and laws of infectious diseases, and the application conditions of the various models differ. The accuracy may differ when using different prediction models for fitting the incidence of the same infectious disease . Currently, a variety of methods are being used in combination to study the pathogenesis of Hepatitis B. For example, systematic reviews, meta-analysis of literature databases, DNA big data analysis,

Challenges we ran into

As we were running out of time we were unable to beautify our frontend and make it look better,most of the time went into making the model predict through testing and training the model with the help of linear regression , KNN, and decision tree.
We have been working on making a section for each user as per login also for future reference we can improve the workflow to work with finding good measures for predicting such diseases.