Penerapan Algoritma Decision Tree Dengan Optimasi Parameter Dalam Memprediksi Reaksi Autoimun Akibat Obat - saifudin; Fadlilah, Nuzul Imam; Nouvel, Ahmad; Sunanto, Sunanto (Informatics and Computer Engineering Journal, 2025)
In the latest developments in medical technology, machine learning, especially the application of the Decision Tree algorithm, is becoming an increasingly popular approach for large-scale health data analysis. Decision Tree is known for its ability to identify hidden patterns in clinical data with interpretation that is easy for medical professionals to understand. Through the process of parameter optimization, the accuracy of the model can be significantly improved, allowing for more precise predictions of possible autoimmune reactions due to the use of certain drugs. The use of Decision Tree-based predictive models with optimized parameters not only strengthens clinical decision-making, but also paves the way for more personalized and precise treatment practices. Parameter optimization is used for the execution of all parameter variations that are set through its subprocesses. The final result recorded an optimal predictive performance of 77.50% with 98.28% more precision for the "true=0" class compared to the "true=1" class.