Boosting Algorithm for Classifying Heart Disease Diagnose


Gunti Pratama, Patrik and Rahman Wijaya, Dedy and Nugroho, Heru and Kannan, Rathimala (2022) Boosting Algorithm for Classifying Heart Disease Diagnose. In: 2022 International Conference on Data Science and Its Applications (ICoDSA), 6-7 July 2022, Bandung, Indonesia.

[img] Text
Boosting_Algorithm_for_Classifying_Heart_Disease_Diagnose.pdf - Published Version
Restricted to Repository staff only

Download (988kB)


The heart is a component of the human body that is responsible for pumping blood and distributing oxygen throughout the body. Hospitals and doctors are still checking heart disease diagnoses manually at this time. However, this method is expensive and time-consuming. In this study, the Gradient Tree Boosting (GTB) algorithm was used to detect patients diagnosed with heart disease (disease and no disease). The purpose of the method is to provide convenience to obtain early information on heart health. With the dataset provided from the UCI Machine Learning Repository, there are 13 supporting features to detect heart disease with a total of 304 data. This study uses the GTB model with the best four parameters and utilizes feature selection which is used to classify. From the results of the study to get a recall score of 0.98, the proposed method succeeded in classifying patients who were diagnosed with heart disease correctly.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Heart, Machine learning algorithms, Hospitals, Data science, Boosting, Feature extraction, Classification algorithms
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions: Faculty of Management (FOM)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 06 Oct 2022 03:48
Last Modified: 06 Oct 2022 03:48


Downloads per month over past year

View ItemEdit (login required)