CNN- GRU algorithm-based chronic kidney disease prediction and classification

Numerous disorders related to lifestyle choices and environmental factors are prevalent among humans today. Predicting and detecting these diseases early on is essential to halting their spread and severity. For physicians, accurately diagnosing illnesses can be challenging. Specifically, one of the key origins of morbidity and death from non-communicable diseases that impact 10-15% of the global population is chronic kidney disease, or CKD. Still, making medical predictions is a difficult and complex undertaking. Our proposed system uses powerful machine learning algorithms to detect and predict people with prevalent chronic conditions. These methods can enhance classifiers' ability to reliably identify chronic diseases. The dataset collected from Kaggle is a chronic kidney disease dataset, comprising 25 features. The first step is preprocessing and normalization of the dataset. PCA extracts the features of chronic disease. The k-nearest neighbour (KNN) is a feature selection method used to select features. A CNN (convolutional neural network)-GRU (gated recurrent unit) classification algorithm is used to predict disease from the dataset. The predicted result is binary, like "CKD" or "NOT CKD", The classification algorithm efficiently evaluates performance metrics, including precision, accuracy, recall, and an F1 score of 1.0.