Abstract :

Helicobacter pylori (H. pylori) infection is a major cause of gastric disorders such as gastritis, peptic ulcers, and gastric cancer. Early and accurate diagnosis is essential to prevent severe complications and reduce healthcare costs. Traditional diagnostic techniques are invasive, time-consuming, and require expert interpretation. This project proposes an ML-powered prediction system for H. pylori infection using patient clinical, demographic, and laboratory data. Various machine learning models are trained to classify infection status with high accuracy. The system aims to provide a non-invasive, fast, and cost-effective diagnostic support tool. Experimental results show improved prediction accuracy compared to conventional methods. The proposed approach demonstrates the potential of ML in medical decision support systems. This study highlights the effectiveness of data-driven healthcare solutions.