RISK ANALYSIS OF LIVER DISEASE BASED ON HIGH CHOLESTEROL FOOD INTAKE AND ALCOHOL CONSUMPTION USING NEURAL NETWORK MODELS.

Abstract

Backgrounds/Objectives: Liver disease is a growing global health problem, mainly caused by unhealthy dietary habits and excessive alcohol consumption. High intake of cholesterol-rich foods, such as fatty and processed items, can lead to liver dysfunction, meaning the liver does not function properly. When combined with regular alcohol intake, this significantly increases the risk of serious liver conditions. Early identification of individuals at risk is important for prevention and effective treatment. This study aims to analyze and predict liver disease risk based on high cholesterol food intake and alcohol consumption using Artificial Intelligence techniques. Methods/Statistical Analysis: This study uses Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) to classify and predict liver disease risk. The dataset is preprocessed using a median filtering method to remove noise and improve data quality. Important factors considered include cholesterol rich food intake, alcohol consumption frequency, and dietary habits (vegetarian and non-vegetarian). These models are trained to identify patterns and relationships between lifestyle factors and liver health. Findings: The results show that individuals who frequently consume high-cholesterol foods along with regular alcohol intake have a higher risk of liver dysfunction and disease. The comparison of models indicates that CNN performs better than ANN in terms of accuracy and pattern recognition. The study also finds that reducing cholesterol intake and alcohol consumption can improve liver health and lower risk levels.