Hepatitis Detection: Leveraging Logistic Regression, SVM and Decision Trees for Early Diagnosis

Hepatitis is a global public health issue that affects individuals of all age groups. This project focuses on the early detection of hepatitis in both young and elderly populations using machine learning (ML) techniques. The study aims to address the unique challenges posed by hepatitis detection in these age groups and develop a robust and accurate diagnostic tool. This research project will involve collecting and analyzing data from both young and elderly patients with hepatitis. ML algorithms, including logistic regression, support vector machines, and decision trees, will be employed to develop predictive models tailored to each age group. The models will be trained on a diverse range of data, including laboratory results, medical history, and demographic information. The outcomes of this study will contribute to improving hepatitis detection in young and elderly populations. Early diagnosis will enable timely medical intervention and ultimately reduce the burden of hepatitis-related complications. Additionally, the project will showcase the potential of ML in enhancing healthcare and medical decision-making for age-specific disease detection.