Electronic health records (EHRs) in chronic kidney disease classification using LSTM

Chronic Kidney Disease (CKD) is among the most significant global health concerns, particularly in terms of its insidiousness during the first stage of its development and gradual devastation throughout the years. There is a prospect of utilizing Electronic Health Records (EHRs) to improve the outcome due to the ability to address problems at an early stage to deliver the most efficient intervention. The paper presents an intelligent predictive analytics system of healthcare in Abu Dhabi healthcare systems that is built on the EHR data collected. The pipeline of the framework is systematic and it entails data preprocessing, feature extraction and classification. The preprocessing phase is assigned to aligning the data, its coherence, and the removal of redundancies and the handling of missing values across all the EHR datasets. The step is relevant due to the heterogeneous nature of clinical information being rather complex. In summarizing the data, Principal Component Analysis (PCA) is applied to extract the features by subjecting the data to the process to compress the data and retain the most clinical information. This improves the computational and model efficiency and performance by removing noise and redundancy. It is then inputted into the constructed Long Short-Term Memory (LSTM) network due to its learning capabilities which give long-range dependencies and temporal patterns of sequential patient information. Precision, recall and F1-score, are also used to test the effectiveness of the model by determining whether the model is effective in the proper identification of CKD cases. The findings show that LSTM model is better than the traditional classifiers it is more predictive and robust. As highlighted in this paper, advanced deep learning methods might be used on EHR data to aid in the prompt identification of CKD and enhance the clinical decision-making process. The suggested framework is flexible and can be extended and provide useful information on how the framework can be applied in real-life healthcare.