Exploring Deep Learning Approaches for Early Detection of CKD: Trends and Techniques

Abdus Samad ()
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Abdus Samad: Department of Computer Science & IT Abasyn University Islamabad Campus

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 4, 1862-1877

Abstract: This study investigates the application of deep learning models, namely CNN, RNNs, and MLP, for the early prediction of CKD. Early detection of CKD is critical for initiating timely treatment, as the disease can advance with few symptoms. The research leverages a preprocessed Kaggle dataset, divided for training and testing, to assess model performance. Among the models, CNN achieved an impressive 99% accuracy, highlighting its strong feature extraction capabilities. The RNN and MLP models also demonstrated high accuracy, reinforcing the potential of deep learning in enhancing CKD screening processes. This approach can support more personalized and preventive healthcare, potentially improving patient outcomes through earlier interventions.

Keywords: RNN; CKD; Deep Learning; CNN; ANN; LSTM; Performance Optimization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:6:y:2024:i:4:p:1862-1877

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