Machine Learning-Based Prediction and Symptom Classification Using Ct Kidney Dataset with Fire Hawk Optimizer and Predictive Rnn

Abstract

Kidney diseases pose a significant health challenge worldwide, necessitating accurate and timely diagnosis for effective intervention. This research addresses the imperative to predict kidney diseases based on symptoms and classify them into relevant categories using the CT Kidney dataset. Existing methods for kidney disease diagnosis may lack precision and specificity, leading to suboptimal patient outcomes. This research addresses this gap by leveraging machine learning algorithms to enhance the accuracy and efficiency of kidney disease prediction. While previous studies have explored machine learning in healthcare, specifically kidney disease prediction, the incorporation of the CT Kidney dataset and the utilization of the Fire Hawk Optimizer for feature extraction and predictive RNN (PRNN) for prediction represent novel contributions. This research bridges the gap by applying advanced algorithms to a comprehensive dataset, striving to improve the specificity and reliability of kidney disease diagnosis. The research employs meticulous preprocessing techniques, including handling missing values, data cleaning, and feature engineering, to ensure the dataset quality. The Fire Hawk Optimizer is utilized for feature extraction, enhancing the relevance of symptoms in predicting kidney diseases. A PRNN is trained on the dataset, enabling accurate classification of symptoms and disease prediction. The evaluation metrics include accuracy, precision, recall, and F1-score, providing a comprehensive assessment of the algorithmic efficacy. The results demonstrate promising performance of the Fire Hawk Optimizer and RNN in predicting kidney diseases and classifying symptoms.