aspg-3-1368-1791417756 20261008000236 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2023 10 1 Tavleen K. Nagi Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA Abhishek Tomar Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA Deepanshi Jain Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA Surinder Kaur Bharati Vidyapeeth’s College of Engineering, GGSIPU, Delhi, INDIA 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% 2023 08 19 1368 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.100101 https://www.americaspg.com/journal/3/article/1368 https://www.americaspg.com/storage/6139.pdf