Evaluasi Performa Algoritma C4.5 untuk Klasifikasi Gagal Jantung Berbasis Heart Failure Clinical Records dengan RapidMiner
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Abstract
Heart failure is one of the cardiovascular diseases with a high mortality rate, requiring data analysis methods that can support accurate decision-making. The problem addressed in this study is how to evaluate the performance of classification algorithms in predicting heart failure patient conditions based on clinical data. This study aims to evaluate the performance of the C4.5 algorithm in classifying heart failure patients using the Heart Failure Clinical Records dataset. The research method applies a data mining classification approach, where data processing and model training are conducted using RapidMiner software. The research stages include data preprocessing, data splitting, C4.5 decision tree model construction, and performance evaluation using a confusion matrix. The results show that the C4.5 algorithm is able to classify heart failure patient data with good accuracy and produces an interpretable decision tree model. It can be concluded that the C4.5 algorithm is an effective method for heart failure classification based on clinical data.
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