This paper develops a predictive model for diagnosing liver diseases based on patient data such as liver function tests, lifestyle factors, and medical history. Machine learning techniques like decision trees, SVM, and neural networks are used to classify patients as healthy or at risk. The system can help healthcare professionals prioritize high-risk cases for further testing and treatment. Recent advancements in machine learning (ML) have shown promise in transforming medical diagnostics by enabling the analysis of complex datasets and uncovering hidden patterns. ML models can be trained to recognize subtle indicators of liver disease in clinical and demographic data, offering a non-invasive, efficient, and cost-effective alternative to traditional methods. By integrating patient data such as liver function test results, medical history, and lifestyle factors, ML systems have the potential to revolutionize early diagnosis and improve patient outcomes.
