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 detection
and accurate prediction of kidney diseases. By harnessing the potential of a comprehensive healthcare dataset and
leveraging a machine learning model originally developed for kidney disease diagnosis, our methodology integrates
intelligent feature selection techniques. These techniques, including heuristic based feature selection and
evolutionary gravitational search-based feature selection (EGS-FS), allow us to identify the most informative features
for accurate prediction. Classification is performed using our newly designed Intelligent Deep Learning based
Classifier, which is further optimized using the cutting-edge Artificial Gorilla Troops Optimizer algorithm. To
assess the performance of our proposed model, we conduct a thorough evaluation and comparison against existing
methods using a range of statistical measures. Remarkably, our experimental results on the widely recognized
Chronic Kidney Disease dataset showcase an exceptional accuracy value of 99%. This research not only contributes
to the advancement of kidney disease prediction but also provides invaluable insights for efficient patient
management. By embracing this novel approach, clinicians can make informed decisions and revolutionize the field
of kidney disease detection and treatment.