CARDIOVASCULAR DISEASE PREDICTION USING ENSEMBLE CLASSIFICATION ALGORITHM IN MACHINE LEARNING

Abstract

Cardiovascular disease includes a wide range of heart-related illnesses and has surpassed cancer as the top cause of mortality worldwide in recent decades. Many people nowadays are engrossed in their daily lives and engage in various activities while ignoring their health. As a result of their rushed lifestyles and disrespect for their health, the number of people becoming unwell is increasing every day. According to the World Health Organization, heart disease claims the lives of over 31% of the world''''s population. As a result, doctors must be able to predict whether a patient may develop heart illness, but the amount of data collected by the medical sector or hospitals, on the other hand, is so vast that it can be difficult to analyze at times. This research paper assessed several aspects of heart illness and develops a model based on supervised learning methods like Gaussian Naïve Bayes and AdaBoosting algorithm. The purpose of this research is to figure out how to anticipate whether a patient will develop heart disease. The AdaBoosting algorithm achieves a great accuracy score of 95%, according to the data.

Keywords

• heart disease prediction
• gaussiannb
• machine learning
• adaboosting algorithm