New Analytical Approaches and Practical Implications in Senegalese Clinical Data

This paper investigates the landscape of Senegalese clinical data, emphasizing the diversity and unique challenges inherent in its analysis. The motivation behind this study is rooted in the need to understand health trends and improve healthcare within Senegal. Given the specific demographic profiles and medical practices in the country, this research aims to utilize advanced statistical methods to derive meaningful insights from complex data.The study employs Principal Component Analysis (PCA) to reduce data dimensionality while maintaining essential information, thus clarifying intricate relationships between medical variables. Additionally, machine learning techniques, such as the XGBoost algorithm, are applied to predict post-operative complications and map networks of infectious diseases. These methods provide significant advancements in clinical data analysis, revealing critical insights for public health in Senegal.Through detailed case studies, the practical application of these methods is demonstrated, highlighting their potential to enhance patient care and disease prevention. Ultimately, this research underscores the importance of adopting advanced analytical approaches tailored to the Senegalese context, aiming to foster improvements in healthcare and medical research across the country