Exploratory Analysis of Chronic Kidney Disease Features: Southern Libya as a Case Study

Abstract

Chronic Kidney Disease (CKD) is a global health challenge that necessitates improved diagnostic quality and disease management through the use of advanced data processing techniques. This study presents an analysis and preparation of CKD patient data in southern Libya, specifically from kidney centers in the city of Sebha. Data were collected from 1,000 medical records, including 30 clinical and laboratory variables. The dataset revealed varying levels of missing values, necessitating the application of preprocessing methodologies to ensure data quality and suitability for analysis. After excluding irrelevant variables, statistical and analytical techniques were applied to identify the biomarkers most strongly associated with disease progression. This serves as a foundation for developing predictive models based on artificial intelligence to enhance early diagnosis and support medical decision-making. The findings emphasize the importance of data preparation and cleaning as a fundamental step in leveraging machine learning techniques in healthcare, contributing to improved care quality and a reduction in the health and economic burden of CKD patients.