2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 10 , Issue 1 , PP: 08-19, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Tavleen K. Nagi 1 * , Abhishek Tomar 2 , Deepanshi Jain 3 , Surinder Kaur 4
Doi: https://doi.org/10.54216/FPA.100101
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%
Cardiovascular disease , Machine Learning , Disease Prediction
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