HEART DISEASE PREDICTION USING NAÏVE BAYES CLASSIFIER ALGORITHM

JU, HENGKY (2023) HEART DISEASE PREDICTION USING NAÏVE BAYES CLASSIFIER ALGORITHM. Other thesis, UNIVERSITAS KHATOLIK SOEGIJAPRANATA.

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Abstract

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.

Item Type:	Thesis (Other)
Subjects:	000 Computer Science, Information and General Works > 004 Data processing & computer science
Divisions:	Faculty of Computer Science > Department of Informatics Engineering
Depositing User:	Mr Yosua Norman Rumondor
Date Deposited:	05 Oct 2023 06:19
Last Modified:	05 Oct 2023 06:19
URI:	http://repository.unika.ac.id/id/eprint/32959

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