Research Article
Cardiovascular Disease Predictor
@INPROCEEDINGS{10.4108/eai.16-4-2022.2318172, author={Daksh Jain and Vardan Yadav and Manavi Kumari and Kundan Chandravansi and Gurakonda Konda Reddy and Jeba Nega Cheltha}, title={Cardiovascular Disease Predictor}, proceedings={Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India}, publisher={EAI}, proceedings_a={THEETAS}, year={2022}, month={6}, keywords={cardiovascular disease prediction gaussiannb knn random forest decisiontree ridgeclassifier}, doi={10.4108/eai.16-4-2022.2318172} }- Daksh Jain
Vardan Yadav
Manavi Kumari
Kundan Chandravansi
Gurakonda Konda Reddy
Jeba Nega Cheltha
Year: 2022
Cardiovascular Disease Predictor
THEETAS
EAI
DOI: 10.4108/eai.16-4-2022.2318172
Abstract
Heart disease is a very common disease that kills many people every year regardless of age. So, machine learning is used to predict if a person has cardiovascular disease. The classification models used here are KNN, Ridge Classifier, Decision Tree, Random Forest and GaussianNB. The dataset used is the Cleveland heart dataset. In this paper, we firstly pre-process the data. After that, the dataset is branched into two parts, the training and the testing parts. After that, we fit the data in the models and get an initial idea prediction. Then, we apply hyperparameter tuning to increase the accuracy of the models. After hyperparameter tuning, we find that KNN performed best with the roc_auc score of 93.2%. This accuracy is an improvement over previous work. A web application using Flask is also created to provide GUI to users.