HEART DISEASE PREDICTION USING MACHINE LEARNING

Cardiovascular disease is still a leading cause of death throughout the world with the imperative need for effective early diagnosis in mitigating significant health risks. It is argued herein that an apparatus of a machine learning framework utilising the Gradient Boosting algorithm would make efficient heart disease predictions.. The system overcomes noisy data, excessive false positives, and computational intensity by utilizing strong pre processing of data, feature extraction, and iterative model fitting. The outcomes prove enhanced diagnostic precision, presenting a valid decision-support aid for clinicians. The project shows the potential of machine learning to revolutionize the diagnosis and treatment planning of heart disease.

 

 

Keywords: Age, cholesterol, and blood pressure. It also have algorithms such as SVM, Decision Trees, and Random Forests are commonly used. Gender, heart rate, logistic regression, random forest, precision, recall, and F1 score measure model performance. Assist doctors in diagnosis and better decision-making.

Age cholesterol and blood pressure. It also have algorithms such as SVM Decision Trees and Random Forests are commonly used. Gender heart rate logistic regression random forest precision recall and F1 score measure model performance. Assist doctors in diagnosis and better decision-making.