Hemochromatosis Pathogenesis and Its Association with Liver Disease: An Analysis Through Machine Learning

A significant risk factor for the development of liver disease is hemochromatosis, an inherited iron overload illness, but the complex pathophysiology behind this link is still unknown. To understand the intricate interactions between hemochromatosis and liver disease, this study uses a cutting-edge strategy that incorporates machine learning techniques. Promising prediction skills are demonstrated by machine learning models, which open up new possibilities for early detection and individualized intervention approaches. The results shed important light on the relationship between hemochromatosis and liver disease and improve our understanding of the pathogenesis of the disease. This study clarifies the pathophysiology of hemochromatosis and its subsequent connection to liver disease, highlighting the revolutionary power of machine learning in understanding intricate biological processes. This selected dataset contains 15 parameters and has 202 dated records with a target value of ‘Disease Name’ which has 5 different categories Type 1, 2, 2A, 2B,2C, 3, 4, and Type5. The experiment is performed using two classifiers including KNN and Naïve Bayes. The k-fold cross-validation technique is also used to improve the performance. After the comparative analysis of the resulting accuracy percentage of different classifiers, we have proposed the model with the classifier with the highest accuracy. Our proposed model has achieved an accuracy rate of 98.00% using a K-NN classifier. Rapid miner platform is used for the application of the machine learning tools and techniques for this research These discoveries have consequences that go beyond clarifying the mechanisms behind disease; they also open the door to better clinical care and tailored treatment therapies for high-risk people.