Model Prediksi Penyakit Ginjal Menggunakan Algoritma Neural Network - Khoirul Mustofa; Linda Widyaningrum; Wiji Lestari; Tri Yusnanto (Lembaga Pengembangan Kinerja Dosen, 2025)
Neural networks are the focal point of machine learning research. Their applications span across various fields and solve complex and intricate problems. Neural networks have now been applied in health image processing to detect various diseases such as cancer and diabetes. Another disease that threatens our health is kidney disease. This disease is becoming more common due to the substances and elements we consume. Death is imminent and inevitable within a few days without at least one functioning kidney. Ignoring kidney damage can lead to chronic kidney disease leading to death. Often, Kidney Disease and its symptoms are mild and gradual, often going unnoticed for years and only being realized recently. This study used 400 patients with 10 attributes as our dataset from Bade General Hospital. We used a Neural network model to predict the presence or absence of kidney disease susceptibility in patients. The model yielded an accuracy of 98%. Furthermore, we identified and highlighted the important features to rank the features used in kidney disease prediction. The results revealed that two attributes; Creatinine and Bicarbonate had the highest influence on kidney disease susceptibility prediction.