Improve BiLSTM algorithm for kidney disease prediction

Kidney disease is a popular and serious health problem that need an accurate and quickly diagnosis in order to effectively treat. The ATBLKDPM model explained in this work can accurately predict renal disease. The proposed model successfully recognizes complicated patterns in patient data by utilizing the sequential processing powers of BiLSTM layers and benefits of attention processes, which leads to more accurate kidney diseases predictions. Using MFMKD2024 dataset, the model was trained and verified, resulting in a 99.5% accuracy rate. Given its early diagnosis and high accuracy, the Attention Triple BiLSTM model holds considerable potential for improving kidney disease treatment outcomes. The outcomes emphasize the model's ability to enhance clinical decision-making processes, which will eventually result to improved medical treatment and management.