PENERAPAN TUNING HYPERPARAMETER RANDOMSEARCHCV PADA ADAPTIVE BOOSTING UNTUK PREDIKSI KELANGSUNGAN HIDUP PASIEN GAGAL JANTUNG - Tita Aulia Edi Putri; Tatik Widiharih; Rukun Santoso (Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, 2022)
Heart failure is the number one cause of death every year. Heart failure is a pathological condition characterized by abnormalities in heart function, which results in the failure of blood to be pumped to supply metabolic needs of tissues. The application of data mining and computational techniques to medical records can be an effective tool to predict each patient's survival who has heart failure symptoms. Data mining is a process of gathering important information from big data. The collection of important information is carried out through several processes, including statistical methods, mathematics, and artificial intelligence technology. The AdaBoost method is one of the supervised algorithms in data mining that is widely applied to make classification models. Hyperparameter Optimization is selecting the optimal set of hyperparameters for a learning algorithm. AdaBoost has hyperparameters requiring a classification process set, namely learning rate and n_estimators. RandomSearchCV is a random combination method of selected hyperparameters used to train the model. This research uses heart failure patient data collected at the Faisalabad Institute of Cardiology and at the Allied Hospital in Faisalabad (Punjab, Pakistan) from April to December 2015. The research uses learning rate: [-2.2] (log scale), n_estimators start from 10 to 776, and Kfold=5 and produces the best hyperparameters in learning rate=0.01 and n_estimators=443 with an accuracy value of 0.85 and AUC value of 0.897.