Early Detection of Hepatitis C Virus Using Machine Learning

Hepatitis C virus (HCV) infection poses a significant threat to global public health, often leading to severe liver complications if left untreated.The timely detection of HCV is of great importance for early crumple and offering to a sufferer, which, in return, improves the outcome.This study looks at an innovative way of conducting early HCV detection with the help of data analysis, which has its approach way more advanced than previous cases and is machine learning.By analyzing and visualizing relevant features from clinical and laboratory datasets, we aim to identify potential indicators of HCV infection.We employ the random forest algorithm, known for its effectiveness in classification tasks, to refine our predictive models.Additionally, preprocessing techniques such as SMOTE for class imbalance and PCA for feature extraction are utilized to enhance data quality and representation.Through ensemble learning, we seek to improve the accuracy of HCV estimation, facilitating better risk assessment and early intervention.Furthermore, we develop a user-friendly graphical user interface using Python Flask, ensuring accessibility for healthcare professionals.Our study contributes to the advancement of early detection methods for HCV, ultimately leading to improved patient care and disease management.