In recent years, the intersection of artificial intelligence (AI) and healthcare has given rise to innovative solutions that not only enhance patient care but also provide valuable insights for healthcare professionals. This article will guide you through the development of a comprehensive AI-enhanced healthcare application using Python, Flask, scikit-learn, and Plotly. The step-by-step guide aims to offer a detailed understanding of the process involved in creating a predictive analytics tool with a user-friendly web interface, data preprocessing, model training, and result visualization.
Overview and Concepts
The primary objective of the healthcare application is to leverage AI to predict patient outcomes based on certain input parameters, such as age and cholesterol levels. The outcome is a binary value (0 or 1) to indicate the absence or presence of some event. Event can be for example high chances of heart attack or stroke if the outcome is one. We’ll create a user-friendly web interface using Flask, a micro web framework in Python. The application will employ a simple logistic regression model from scikit-learn for predictive analytics. Visualizations are generated using Plotly to enhance data interpretation.
