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Is your feature request related to a problem? Please describe.
Hepatitis is a serious liver disease that can be life-threatening if not detected early. Traditional diagnosis relies on a combination of symptoms and complex laboratory tests, which may delay treatment. This project addresses the challenge of predicting hepatitis risk based on accessible health metrics, enabling faster, cost-effective, and non-invasive pre-screening methods. By building a predictive model, this project aims to support healthcare providers in identifying patients at risk of hepatitis, thereby contributing to early diagnosis and improved patient care.
Describe the solution you'd like
The "Hepatitis Prediction Model" is a machine learning application designed to predict hepatitis presence based on various patient health metrics. Using a Random Forest classifier, this model identifies patterns in historical patient data to classify whether a patient is at risk of hepatitis or not. The model aims to assist healthcare providers by offering a tool to help in early detection of hepatitis, potentially improving patient outcomes through timely intervention.