Analisa Data Mining Untuk Prediksi Penyakit Ginjal Kronik Dengan Algoritma Regresi Linier - Angga Kurniadi Hermawan; Agung Nugroho; Edora (Forum Kerjasama Pendidikan Tinggi (FKPT), 2023)
In this study, we evaluate the ability of data mining to predict chronic kidney disease using a linear regression algorithm. We extract features from patient clinical data and apply a linear regression algorithm to build a predictive model. The results showed that our linear regression model was able to predict with high accuracy and could be used as an aid in diagnosing chronic kidney disease. In addition, we also analyze the factors that influence the risk of developing chronic kidney disease and suggest preventive measures that can be taken to reduce the risk of developing the disease. The results of this study can be used by doctors to improve efficiency in diagnosing and preventing chronic kidney disease. In addition, these results can also be used as a basis for further research in the field of data mining and chronic kidney disease. The process of testing the data in this study using a linear regression algorithm is able to provide good results with a Root Mean Squared Error: 0.285 +/- 0.000 and Squared Error: 0.081 +/- 0.234.