PREDICTION OF BLADDER CANCER USING CLINICAL LABORATORY DATA

Abstract

To improve diagnostic and prognosis accuracy, bladder cancer prediction utilizing clinical laboratory data integrates a number of cutting-edge approaches, such as deep learning, radiomics, and machine learning. To improve diagnostic accuracy, a variety of machine learning approaches and data sources must be integrated when predicting bladder cancer using clinical laboratory data. Due to the disease's complexity and heterogeneity, bladder cancer prediction using clinical laboratory data presents several difficulties.By combining machine learning and multi-modal data analysis, bladder cancer prediction using clinical laboratory data has advanced significantly. The combination of multi-modal data, which integrates clinical, imaging, histological, molecular, and genomic insights, has greatly improved the prediction of bladder cancer. Clinical, genetic, and computational approaches are among the categories that must be integrated in order to predict bladder cancer using clinical laboratory data. By merging patient demographics, medical history, imaging, and biomarker data, a multi-modal clinical data integration approach effectively improves the predictive ability of bladder cancer models.