Assessing the urinary lithogenic risk by multivariate data analysis using analytical results and historic archives

Abstract

[EN] Exploratory Data Analysis (EDA): explore the database from an univariate perspective to analyse the distribution of the data. Shed light on the nephrolithiasis process by studying it from a multivariate, interdisciplinary perspective, analysing the properties and characteristics of the variables collectively together with their importance through PCA. Calculate and interpret the correlations and interactions between every variable and grouped in clusters. Use the correlations and interactions among variables to train an AI-based model for the clinical diagnosis and prevention of urolithiasis that can be implemented in hospitals and primary attention clinics. Make the AI-based model capable of predicting the probability that a problem patient has kidney stones and predict which type