Abstract:
Heart disease is a major risk to public health, accounting for 17.9 million deaths globally
annually. Predicting cardiac disease with machine learning (ML) approaches like Linear
Regression, Random Forests, Naive Bayes, Decision Tree, Neural Network, XGBoost,
AdaBoost, Support Vector Machines, and Catboost models, has shown encouraging results
and we use ensemble model from the best model depending on their accuracy and others
factors. And we find the best accuracy 97.53% from KNN & RF combined. These
algorithms use large medical data to provide personalized risk assessment models for each
unique user. As a result, heart disease may have a lessening impact on international health
systems and improve methods for risk assessment and treatment. The association between
cholesterol and fasting blood sugar levels and heart attacks was shown to be the weakest.
The risk factors for heart disease are older adults and men. However, it is important to note
that these ML approaches are not foolproof and may have limitations in accurately
predicting all cases of heart disease. Additionally, further research and development in this
area are necessary to enhance the accuracy and reliability of these personalized risk
assessment models. It is crucial for healthcare professionals to continue monitoring and
staying updated on the latest advancements in ML technology to effectively prevent and
treat heart disease in individuals at risk.
