Fixdes #816 Hepatitis Prediction Model

Prediction Models/Hepatitis Prediction/Readme.md

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+# Hepatitis Prediction Model
+
+## Project Overview
+The Hepatitis Prediction Model uses machine learning, specifically a Random Forest classifier, to predict the likelihood of hepatitis in patients. This model was trained on historical patient data and includes various features that represent patient health metrics. The goal is to provide a tool for early detection, aiding in timely intervention and improving patient outcomes.
+
+## Problem Statement
+Hepatitis is a potentially deadly disease that can be mitigated with early diagnosis. Traditional diagnostic methods can be costly and time-consuming, often requiring multiple tests and symptom evaluations. This model provides a predictive solution based on health data, offering a quicker and more accessible pre-screening option to identify at-risk patients.
+
+## Features
+- Random Forest classifier for hepatitis prediction
+- Health metrics and risk factors as input features
+- Model evaluation through accuracy, precision, and recall metrics
+
+## Dataset
+The model is trained on a dataset containing patient health data, including demographics and various health indicators relevant to hepatitis risk.