📊Predictive Modeling

Predictive modeling uses data-driven algorithms to predict future outcomes based on historical patterns and trends.

The Pulse AI model leverages the power of historical data sourced from a number of publicly available repositories, including sources such as WebMD, Drug.com, to compile detailed data tailored to specific diseases and symptoms.

Using a variety of machine learning algorithms like Decision Tree Regressor, each optimized to address a specific aspect of health prediction, our platform seeks to discover complex interactions between symptoms , between diagnosis and outcome. These carefully constructed and rigorously trained models seek to recognize subtle patterns embedded in the much broader medical context. By using advanced statistical techniques and SOTA computational techniques, we aim to unlock the hidden potential of these datasets, and extract the actionable insights they can provide they have made informed decisions about health. Which helps in prediction of diseases and recommend medication. Also giving predictive values for probabilities of diseases like Diabetes

PULSE AI PREDICTIVE MODELS

Tools/Tech Used: Python, Pandas, NumPy, and Scikit-learn, TensorFlow, and PyTorch.