Cardio Sense AI

Predicting heart disease risk using machine learning with clinical and demographic features and explore the factors influencing heart health and build predictive models.

Project Description:

Cardio Sense AI is a machine learning project aimed at predicting the risk of heart disease based on a combination of clinical and demographic features. It also explores the factors that influence heart health, providing valuable insights for healthcare professionals and decision-makers. This project leverages data analytics and predictive modeling techniques to enhance patient outcomes and reduce healthcare costs.

Goals of the Project:

Predictive Modeling:

 Develop and train machine learning models to predict the risk of heart disease using clinical and demographic features.

Data Exploration:

 Explore the dataset to identify patterns, correlations, and factors that contribute to heart health or disease.

Animation :

 Create effective Animation using lottie to communicate insights and trends related to heart health.

Decision Support:

 Assist hospital administrations in making informed decisions related to resource allocation and patient care strategies.

Skills Covered:

Data Preprocessing: Cleaning, transforming, and preparing data for analysis.

Machine Learning: Building predictive models for healthcare risk assessment.

Data Exploration: Identifying trends and patterns in healthcare data.

Data Visualization: Creating clear and informative visuals for data presentation.

Healthcare Analytics: Applying data analytics techniques to healthcare domain.

Communication: Effectively conveying insights and recommendations to stakeholders.

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