An Analysis of Disease Prediction and Symptom Classification Based on Machine Learning with the CT Kidney Dataset

Kidney diseases are one of the major causes of morbidity for people around the world, and early diagnosis with the help of medical imaging is essential for successful clinical intervention. However, it has been challenging to accurately predict kidney disease using computed tomography (CT) images, because of the greatest inter-class similarity, imaging noise, and scanner and patient population variability. This study aims to build a framework for advanced and robust prediction that is able to overcome these limitations using intelligent learning and optimization strategies. An evolutionary optimization-based deep ensemble learning deep learning approach is proposed, which integrates attention-guided kidney segmentation, multi-scale deep feature extraction, evolutionary feature selection & hyperparameter tuning, and adaptive ensemble classification. The framework is evaluated on a publicly accessible dataset of kidney CT scans, for which it shows superior results v.s. various state-of-the-art methods, achieving an accuracy of 96.45%, sensitivity of 95.62% and specificity of 97.10% and an AUC of 0.982. The results show that evolutionary optimization with deep ensemble learning has a great potential to improve the reliability of diagnosis and provides a clinically interpretable and effective solution for automated kidney disease diagnosis.