This Jupyter notebook, cirrhosis-outcomes.ipynb, offers a comprehensive analysis of outcomes related to cirrhosis. It uses various data science techniques to explore, visualize, and interpret data, providing insights into factors influencing cirrhosis outcomes.
Cirrhosis, a chronic liver disease, poses significant healthcare challenges. This notebook aims to deepen our understanding of cirrhosis through data-driven analysis. We use Python libraries like Pandas, Matplotlib, and Scikit-learn for data manipulation, visualization, and predictive modeling.
Data Cleaning and Preprocessing: Techniques to prepare the dataset for analysis. Exploratory Data Analysis (EDA): Visualizing data to uncover patterns and trends. Statistical Analysis: Applying statistical methods to infer insights from the data. Predictive Modeling: Building models to predict cirrhosis outcomes.
Healthcare Research: Useful for medical researchers studying cirrhosis, providing a framework for analyzing clinical data. Educational Purposes: A resource for students and educators in fields like biostatistics, epidemiology, and health data science. Policy Making: Assists health policymakers in understanding the impact of cirrhosis and in making informed decisions. Patient Care Improvement: Helps healthcare providers identify key factors affecting patient outcomes, improving care strategies.