Abstract
Congestive heart failure (CHF) in canines, primarily caused by myxomatous mitral valve disease, is a prevalent condition affecting many dogs. It presents challenges in diagnosis, treatment, and prognosis. This project aims to develop a comprehensive dashboard that assists pet owners and veterinarians manage CHF in dogs by integrating state-of-the-art machine learning algorithms, data visualization techniques, and research findings on CHF in dogs.
The dashboard incorporates research findings from various studies, including randomized clinical trials, consensus guidelines, population and survival characteristics, and pathophysiological mechanisms involved in the disease process. By synthesizing this knowledge, the dashboard facilitates evidence-based decision-making in clinical practice, ultimately improving affected dogs' quality of life and survival rates.
The technical backbone of the dashboard comprises Python, a widely used programming language in data science and machine learning. Machine learning algorithms, such as the Random Forest Classifier, are implemented using the scikit-learn library to predict CHF outcomes and identify critical prognostic factors. Efficient data manipulation and analysis are achieved using the Pandas and NumPy libraries, which provide high-performance data structures and array programming capabilities.
Visually appealing and informative data representation is facilitated through Streamlit and Plotly libraries, enabling the creation of various plots and charts. The development environment used for this project will be PyCharm, an integrated development environment that supports Python and offers various tools for efficient coding and debugging. Finally, the dashboard was developed using Streamlit, a Python library that allows the rapid creation of web applications and interactive data visualization.
In conclusion, the comprehensive dashboard for managing CHF in dogs integrates research findings, state-of-the-art machine learning algorithms, and data visualization techniques.