Heart Failure Prediction is a machine learning project that predicts the likelihood of heart failure based on various features related to the patient's health. The project uses a dataset from Kaggle and applies several techniques such as feature scaling, train-test splitting, and multi-collinearity using VIF to preprocess the data. It then trains the dataset using several classifiers such as logistic regression, support vector machine, k-nearest neighbor, and random forest classifier. The healthcare industry collects vast amounts of data containing hidden information that can be useful for making effective decisions. Advanced data mining techniques are used to provide appropriate results and make informed decisions based on the data. In this study, an Effective Heart Disease Prediction System (EHDPS) has been developed using a neural network to predict the risk level of heart disease.The EHDPS uses 15 medical parameters, including age, sex, blood pressure, cholesterol, and obesity, for prediction. It enables significant knowledge, such as relationships between medical factors related to heart disease and patterns, to be established. The multilayer perceptron neural network with backpropagation is employed as the training algorithm.The results demonstrate that the designed diagnostic system can effectively predict the risk level of heart diseases. This system can aid healthcare professionals in identifying patients who are at high risk of developing heart disease and take preventative measures to minimize the risk. Overall, if we implant this idea in market that can lead to better patient outcomes and improve the quality of care in the healthcare industry.
Installation There is no installation required for this project. The front-end application is available where users can input their necessary information regarding their body and get the prediction result.
Contributing If you would like to contribute to this project, please follow these guidelines:
Fork the repository Create a new branch (git checkout -b feature/) Make changes and add them with git (git add ) Commit your changes (git commit -m "Add feature") Push to the branch (git push origin feature/) Create a pull request Credits This project was developed by Aniket Chatterjee along with Jyotirmoy Sarkar, Utkarsh Prakhar, Amitansu Purohit
Contact If you have any questions or concerns, please contact Aniket Chatterjee at aniketchatterjee999@gmail.com.