Henry Gas Optimization Algorithm with Deep Learning based Chronic Kidney Disease Detection and Classification Model

Abstract

Chronic Kidney Disease (CKD) is a progressive condition that may cause kidney failure, so earlier diagnosis is critical for proper management. The condition has a large fatality, particularly in developing nations. CKD often remains unnoticed because there is no obvious earlier-stage symptom. Meanwhile, earlier diagnosis and on-time clinical intervention are essential for reducing the disease progression. Detecting CKD using deep learning (DL) methods and feature selection (FS) could be a useful application of artificial intelligence (AI) in healthcare. DL algorithms can provide cost-effective and efficient computer-aided diagnoses (CAD) to assist clinicians in accomplishing earlier CKD recognition. Therefore, this study develops an automated CKD detection using Henry Gas Optimization Algorithm with DL (CKDD-HGSODL) approach. The drive of the CKDD-HGSODL approach is to classify and detect the presence of CKD utilizing FS and hyperparameter tuning strategies. In the presented CKDD-HGSODL technique, min-max scaling can be used to normalize the input data. In addition, the CKDD-HGSODL technique utilizes the HGSO model for selecting optimal features. For the CKD detection process, an attention-based gated recurrent unit (AGRU) model can be utilized. At last, the slime mould algorithm (SMA) can be utilized for the optimal hyperparameter selection of the AGRU approach that aids in improving the classifier results. To validate the outcome of the CKDD-HGSODL approach, a comprehensive simulation value is made on the benchmark CKD database. The obtained performances depict the enhanced detection results of the CKDD-HGSODL approach on CKD diagnosis.