HomeAssessmentAbout Us

Our Goal

To develop a web application that employs a Bayesian algorithm to accurately classify individuals as either having liver disease or not, based on a given dataset of patient information. This tool aims to assist in early detection and potential prevention of liver disease, contributing to improved healthcare outcomes.

Algorithm Used

Naive Bayes is a probabilistic algorithm used to predict liver disease based on a specific set of medical parameters such as patient age, gender, and biochemical markers. The model makes use of eleven key features, including total and direct bilirubin levels, liver enzyme tests such as alkaline phosphatase, alamine aminotransferase, and aspartate aminotransferase, as well as total proteins, albumin, and albumin-globulin ratio. The algorithm calculates the probability of liver disease based on these precise biochemical and demographic indicators. The method works by looking at how these individual parameters statistically correlate with the presence or absence of liver disease, allowing for a computational approach to initial medical screening that can help identify potential risk patterns across different patient profiles.