Heart Failure Prediction System [Ref: TT10095]

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Heart failure prediction project focused on developing a deployable machine learning model to predict the onset of heart failure in patients.

This project includes the development of a core model and the implementation of a chatbot complete with a front and back end and API endpoints for serving the model to patients and the backend for sending notifications to doctors

According to the World Health Organization, 12 million deaths occur yearly due to heart disease. Load of cardiovascular disease is rapidly increasing all over the world in the past few years. Early detection of cardiac diseases can decrease the mortality rate and overall complications. However, it is not possible to monitor patients every day in all cases accurately and consultation with a patient for 24 hours by a doctor is not available since it requires more patience, time and expertise.

Our Heart Failure Prediction System is intended to assist patients in recognizing their heart state early and receiving treatment at an earlier stage, allowing them to avoid any serious conditions. We have designed this system using the Machine Learning model to predict the future possibility of heart disease by implementing the Logistic Regression algorithm.
The framework used in this project is Django. The Front End involves Html, CSS and JavaScript. The Back End involves MySQL Database. The Back End Language is Python

The user would need to register first to log into the system. For the system to predict if there is heart failure or not, the user would require to give inputs. The parameters include Age, Sex, Chest Pain Type, Resting BP, Cholesterol, Fasting BS, Resting ECG, Maximum Heart Rate, Exercise-induced Agina, Oldpeak, and the slope of the peak exercise ST segment. After the user provides all these inputs, the system will detect if there is any heart disease. The chatbot in the system will inform the user about the causes of heart failure, and the diagnosis test required. It will also provide links to nearby hospitals/clinics that specializes in heart disease. The user can also check out some free checkup camps.

The admin can log in using their credentials. They can view the users using the system. They can also add free checkup camp details. We have used Logistic Regression to develop this system. It is a significant machine learning algorithm because it can provide probabilities and classify new data using continuous and discrete datasets.

Advantages

• The system is easy to maintain.


• It is user-friendly.


• Users can search for a doctor’s help at any point in time.


• Very useful in case of emergency.


• They can also look for free check-up camps.

Requirements:

Basic knowledge of python required