Synergistic Excellence: CNN- LSTM Hybrid Model for Improved CKD Diagnosis

Chronic Kidney Disease (CKD) is a widespread and potentially fatal ailment that impacts millions of individuals globally. Early detection and timely intervention play a crucial role in preventing disease progression and improving patient outcomes. In recent years, advancement in Machine Learning (ML) and data analytics has shown promising potential for aiding the diagnosis and management of diseases. This study investigates the utilization of the CNN LSTM hybrid model to detect CKD using CSV data. To ensure the study's efficiency, the dataset is collected from the Kaggle repository. The dataset contains 400 samples with 37 different attributes for each sample. The prepared data is utilized for the prediction process, where a CNN is employed. The LSTM network used in this model analyzed the temporal dependencies and patterns in sequential data. The performance of the model was assessed using different performance metrics, resulting in an impressive accuracy rate of 98.75%. The results of this paper carry substantial significance in the progression of Deep Learning (DL) oriented diagnostic instruments for the prompt detection and management of CKD.