Abstract:
According to the WHO, 12 million people die from heart disease annually. Global cardiovascular disease rates have risen substantially in recent years. Early identification of heart disorders reduces mortality and consequences. Monitoring patients daily is not practicable in all circumstances, and doctors cannot consult with patients for 24 hours due to time, patience, and competence.
Our Heart Failure Prediction System helps patients detect and treat heart failure early, preventing catastrophic situations. We created this system utilizing the Machine Learning model and Logistic Regression to forecast cardiac disease.
This project uses Django. The Front End uses HTML, CSS, and JavaScript. The back end uses MySQL. Python is the backend.
The user must register to log in. User inputs are needed for the algorithm to predict cardiac failure. Age, Sex, Chest Pain Type, Resting BP, Cholesterol, Fasting BS, Resting ECG, Maximum Heart Rate, Exercise-induced Agina, Oldpeak, and peak exercise ST segment slope are the factors. After these inputs, the machine will detect heart illness. The system chatbot will explain heart failure causes and tests. It will also link to adjacent heart disease hospitals/clinics. Users can also attend free checkup camps.
Admins can log in with credentials. The system lets them see users. They can also describe free checkup camps. We built this system using Logistic Regression. It provides probabilities and classifies new data using continuous and discrete datasets, making it a key machine learning algorithm.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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