Klasifikasi Penyakit Ginjal Kronis Menggunkan Metode
Random Forest Dengan Hyperparameter Tuning
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Institusion
Universitas Muhammadiyah Malang
Author
Rais, M. Fauzi
Subject
Q Science (General)
Datestamp
2023-11-20 08:47:02
Abstract :
The kidneys play a vital role in filtering metabolic waste from the blood, maintaining fluid and electrolyte balance, and producing important hormones. Chronic Kidney Disease (CKD) is a serious global issue, often referred to as the "silent killer" due to being a leading cause of death. The World Health Organization (WHO) records that CKD causes millions of deaths each year, with the number of affected individuals continuously rising. In an effort to enhance the detection and management of CKD, this research aims to develop a classification model using the Random Forest method with the implementation of hyperparameter tuning. The dataset used in this study originates from the UCI Machine Learning Repository. Hyperparameter tuning aims to find the best configuration that can enhance the model's performance in predicting CKD cases. The results of this research are expected to contribute positively to early detection and management of chronic kidney disease. The outcomes of this study, utilizing Random Forest with Hyperparameter Tuning, resulted in an accuracy of 97%.
The kidneys play a vital role in filtering metabolic waste from the blood, maintaining fluid and electrolyte balance, and producing important hormones. Chronic Kidney Disease (CKD) is a serious global issue, often referred to as the "silent killer" due to being a leading cause of death. The World Health Organization (WHO) records that CKD causes millions of deaths each year, with the number of affected individuals continuously rising. In an effort to enhance the detection and management of CKD, this research aims to develop a classification model using the Random Forest method with the implementation of hyperparameter tuning. The dataset used in this study originates from the UCI Machine Learning Repository. Hyperparameter tuning aims to find the best configuration that can enhance the model's performance in predicting CKD cases. The results of this research are expected to contribute positively to early detection and management of chronic kidney disease. The outcomes of this study, utilizing Random Forest with Hyperparameter Tuning, resulted in an accuracy of 97%.
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