HEART DISEASE PREDICTION USING NAÏVE BAYES CLASSIFIER ALGORITHM

Heart disease is one of the deadliest diseases worldwide based on the number of deaths of sufferers. Heart disease is caused by narrowing and blockage of blood vessels that supply blood and oxygen to the heart. To prevent this impact, this Naïve Bayes method research was made to predict whether someone has heart disease or not based on certain factors that have been created. These factors such as age, sex, cp, trestbps, chol, fbs, restech, thalach, exang, oldpeak, slope, ca, thal, the data is taken from the kaggle data source. After the data is processed, Naïve Bayes is used for training data in predicting heart disease based on the above factors, and then tested using test data that does not exist before. The results of this study show that Naïve Bayes in predicting heart disease gets a fairly good accuracy of 85-90%. This study shows that Naïve Bayes can be used as an effective tool in predicting heart disease based on existing risk factors. With this, medical personnel can identify high-risk patients and provide appropriate interventions. However, further work is needed to test and validate the results in a wider population and consider additional risk factors that affect heart disease prediction