Implementasi Machine Learning Untuk Memprediksi Hepatitis Mortality Menggunakan Algoritma Decision Tree

Abstract

Hepatitis is a disease that affects the liver. The liver is an important organ that processes nutrients, filters blood, and fights infections. Medical diagnosis has important implications for improving patient care, research, and policy. For medical diagnosis, healthcare professionals use different types of pathological methods to make decisions based on medical reports in terms of a patient's medical condition. The use of artificial intelligence and machine learning combined with clinical findings has further improved disease detection. Machine learning algorithms based on specific problems can help one make decisions. Machine learning (ML) and data-driven algorithms can be used to validate existing methods and help researchers make potential new decisions. There are many techniques used in machine learning, one of which is the decision tree algorithm used in this study. A decision tree is a method for approximating discrete-valued target functions, where the learned functions are represented by decision trees. This study presents the resulting data visualization by building a machine learning program to collect the parameters required for modeling and visualizing the data that demonstrates problem solving.