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Fusion: Practice and Applications
Volume 10 , Issue 1, PP: 08-19 , 2023 | Cite this article as | XML | Html |PDF


Identification of Cardiovascular Disease Patients

Authors Names :   Tavleen K. Nagi   1 *     Abhishek Tomar   2     Deepanshi Jain   3     Surinder Kaur   4  

1  Affiliation :  Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :  tavleennagi15@gmail.com

2  Affiliation :  Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :   tomar4349@gmail.com

3  Affiliation :  Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :   jaindeepanshi04@gmail.com

4  Affiliation :  Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA

    Email :  kaur.surinder@bharatividyapeeth.edu

Doi   :   https://doi.org/10.54216/FPA.100101

Received: May 10, 2022 Accepted: October 14, 2022

Abstract :

For the prevention and treatment of illness, accurate and timely investigation of any health-related problem is critical. The prevalence of cardiovascular illnesses is rising among Indians. Aging has long been recognized as one of the most significant risk factors for heart attacks, affecting men and women aged 50 and up. Cardiovascular attacks are increasingly becoming more common in people in their 20s, 30s, and 40s.. To detect and predict cardiovascular disease patients, starting with a pre-processing step in which we used feature selection to pick the most important features, we tested the accuracy of different models on a dataset with features like gender, age, blood pressure, and glucose levels. The model predicts whether a patient is likely to suffer from cardiovascular disease based on their medical records. Finally, we performed hyperparameter tuning to find the best parameter for the models. In comparison to the other algorithms, the XGBoost model produced the best results with an accuracy of 75.72%

Keywords :

Cardiovascular disease; Machine Learning; Disease Prediction 

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Cite this Article as :
Tavleen K. Nagi , Abhishek Tomar , Deepanshi Jain , Surinder Kaur, Identification of Cardiovascular Disease Patients, Fusion: Practice and Applications, Vol. 10 , No. 1 , (2023) : 08-19 (Doi   :  https://doi.org/10.54216/FPA.100101)