Leveraging CNN-Based Image Processing for Accurate Life Expectancy Prediction in Patients with Renal Failure

Chronic kidney disease (CKD) poses significant challenges to healthcare systems worldwide due to its progressive nature and high mortality rates. Accurate life expectancy prediction in patients with renal failure can play a pivotal role in personalized treatment planning and resource allocation. The subject matter of this study revolves around the concepts of CNN in image handling and the assessments of the likely life span of patients diagnosed with CKD. CNNs, known for their powerful feature extraction capabilities, are utilized to analyze medical imaging data, including renal ultrasound and CT scans. The methodology integrates CNNs with clinical data using advanced machine learning frameworks to improve prediction accuracy. This proposed model integrates image and non-image data using multi-modal process of pathway level CNN- based image analysis and applying Recursive Feature Elimination model for feature selection of clinical variables. Before finalizing the model, GridSearchCV is used to tune the best model practicable. The dataset consists of deidentified subject descriptions and records derived from public and private repositories of medical data. Evaluation of them including the accuracy, the precision, the recall, the F1-score, and the Mean of Absolute Error (MAE) show the effectiveness of the proposed model. Preliminary work indicates that the CNN-based strategy provides additional accuracy, with over 92% accuracy and a decrease in the number of errors in comparison to conventional predictive models. The current work highlights the paradigm shift in nephrology through deep learning model and a roadmap that will help in enhancing appointments among patients. Possible future studies may include the expansion of this framework to cover other chronic diseases further enhancing the application of healthcare analytics.