Breaking Barriers in Kidney Disease Detection: Leveraging Intelligent Deep Learning and Artificial Gorilla Troops Optimizer for Accurate Prediction

Abstract

In this groundbreaking study, we propose an innovative approach to tackle the formidable task of early detectionand accurate prediction of kidney diseases. By harnessing the potential of a comprehensive healthcare dataset andleveraging a machine learning model originally developed for kidney disease diagnosis, our methodology integratesintelligent feature selection techniques. These techniques, including heuristic based feature selection andevolutionary gravitational search-based feature selection (EGS-FS), allow us to identify the most informative featuresfor accurate prediction. Classification is performed using our newly designed Intelligent Deep Learning basedClassifier, which is further optimized using the cutting-edge Artificial Gorilla Troops Optimizer algorithm. Toassess the performance of our proposed model, we conduct a thorough evaluation and comparison against existingmethods using a range of statistical measures. Remarkably, our experimental results on the widely recognizedChronic Kidney Disease dataset showcase an exceptional accuracy value of 99%. This research not only contributesto the advancement of kidney disease prediction but also provides invaluable insights for efficient patientmanagement. By embracing this novel approach, clinicians can make informed decisions and revolutionize the fieldof kidney disease detection and treatment.