Performance Analysis of Liver Diseases Classification using Machine Learning Classifier

👥 Neetiraj Singh Lodhi, Dr.Rajneesh Choubey

📙 Abstract : Early detection through supervised learning techniques applied to diverse datasets is crucial in reducing mortality rates. With ongoing advancements in medicine, a significant amount of data has been collected, particularly in healthcare, where extensive data generation occurs. This data is processed and analyzed through data mining applications for knowledge extraction. Mining algorithms effectively predict patient diseases by utilizing appropriate learning strategies. Chronic kidney disease (CKD), hepatitis, cancer, and diabetes represent major global health concerns, making their prediction a significant focus for researchers. This dissertation primarily analyzes various classification algorithms, including Support Vector Machines (SVM), K Nearest Neighbor (KNN), and Extra Tree Classifier, by comparing their performance utilizing liver patient data. The study employs different machine learning classification techniques to diagnose liver conditions at an early stage, yielding performance metrics such as accuracy and other relevant parameters.

🔖 Keywords :️ Accuracy, support vector machines, Supervised machine learning, Random forest.