Menganalisa Metode Association Rules Untuk Menentukan Pola Penyakit Komplikasi Pada Pasien Menggunakan Algoritma FP-Gworth

Coronavirus infection is an acute respiratory. This disease can lead to severe complications, particularly in older individuals or those with chronic illnesses. To understand the patterns of common complications among patients, this study utilizes the FP-Growth algorithm to analyze the relationship between disease histories within a dataset. The algorithm was chosen for its efficiency in processing large-scale data and identifying frequent itemset patterns compared to other algorithms. Using RapidMiner Studio software, this study successfully identified associative rule patterns that serve as references for predicting and preventing disease complications caused virus in the future. The results demonstrate that this method provides fast and accurate outcomes, aiding in decision-making for treatment and prevention strategies.