Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification
Heart disease is one of the leading causes of death worldwide. According to data from the World Health Organisation (WHO), the number of victims who die from heart disease reaches 17.5 million people every year. However, the method of diagnosing heart disease in patients is still not optimal in det...
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| Format: | Article |
| Language: | English |
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Universitas Islam Negeri Sunan Kalijaga Yogyakarta
2025-01-01
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| Series: | JISKA (Jurnal Informatika Sunan Kalijaga) |
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| Online Access: | https://ejournal.uin-suka.ac.id/saintek/JISKA/article/view/4826 |
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| _version_ | 1832574146257616896 |
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| author | Ahmad Ubai Dullah Aditya Yoga Darmawan Dwika Ananda Agustina Pertiwi Jumanto Unjung |
| author_facet | Ahmad Ubai Dullah Aditya Yoga Darmawan Dwika Ananda Agustina Pertiwi Jumanto Unjung |
| author_sort | Ahmad Ubai Dullah |
| collection | DOAJ |
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Heart disease is one of the leading causes of death worldwide. According to data from the World Health Organisation (WHO), the number of victims who die from heart disease reaches 17.5 million people every year. However, the method of diagnosing heart disease in patients is still not optimal in determining the right treatment. Along with the development of technology, various models of machine learning algorithms and data processing techniques have been developed to find models that can produce the best precision in classifying heart disease. machine learning algorithm model in classifying heart disease, so that it can improve the effectiveness of diagnosis and help in determining the right treatment for patients. This research also aims to overcome the limitations of accuracy in existing diagnosis methods by identifying models that are capable of providing the best results in processing and analysing health data, especially in terms of heart disease classification. In this study, the XGBoost model was identified as the most superior, with an accuracy of 99%. These results show that the XGBoost model has a higher accuracy rate compared to previous methods, making it a promising solution to improve the accuracy of heart disease diagnosis and classification in the future.
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| format | Article |
| id | doaj-art-1521f7efe16342bdb2a79e589f1fd8a5 |
| institution | Kabale University |
| issn | 2527-5836 2528-0074 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Islam Negeri Sunan Kalijaga Yogyakarta |
| record_format | Article |
| series | JISKA (Jurnal Informatika Sunan Kalijaga) |
| spelling | doaj-art-1521f7efe16342bdb2a79e589f1fd8a52025-02-02T00:37:09ZengUniversitas Islam Negeri Sunan Kalijaga YogyakartaJISKA (Jurnal Informatika Sunan Kalijaga)2527-58362528-00742025-01-01101Extreme Gradient Boosting Model with SMOTE for Heart Disease ClassificationAhmad Ubai Dullah0Aditya Yoga Darmawan1Dwika Ananda Agustina Pertiwi2Jumanto Unjung3Department of Computer Science, Universitas Negeri Semarang, IndonesiaDepartment of Computer Science, Universitas Negeri Semarang, IndonesiaFaculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, MalaysiaDepartment of Computer Science, Universitas Negeri Semarang, Indonesia Heart disease is one of the leading causes of death worldwide. According to data from the World Health Organisation (WHO), the number of victims who die from heart disease reaches 17.5 million people every year. However, the method of diagnosing heart disease in patients is still not optimal in determining the right treatment. Along with the development of technology, various models of machine learning algorithms and data processing techniques have been developed to find models that can produce the best precision in classifying heart disease. machine learning algorithm model in classifying heart disease, so that it can improve the effectiveness of diagnosis and help in determining the right treatment for patients. This research also aims to overcome the limitations of accuracy in existing diagnosis methods by identifying models that are capable of providing the best results in processing and analysing health data, especially in terms of heart disease classification. In this study, the XGBoost model was identified as the most superior, with an accuracy of 99%. These results show that the XGBoost model has a higher accuracy rate compared to previous methods, making it a promising solution to improve the accuracy of heart disease diagnosis and classification in the future. https://ejournal.uin-suka.ac.id/saintek/JISKA/article/view/4826Heart DiseaseClassificationSMOTEXGBoost |
| spellingShingle | Ahmad Ubai Dullah Aditya Yoga Darmawan Dwika Ananda Agustina Pertiwi Jumanto Unjung Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification JISKA (Jurnal Informatika Sunan Kalijaga) Heart Disease Classification SMOTE XGBoost |
| title | Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification |
| title_full | Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification |
| title_fullStr | Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification |
| title_full_unstemmed | Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification |
| title_short | Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification |
| title_sort | extreme gradient boosting model with smote for heart disease classification |
| topic | Heart Disease Classification SMOTE XGBoost |
| url | https://ejournal.uin-suka.ac.id/saintek/JISKA/article/view/4826 |
| work_keys_str_mv | AT ahmadubaidullah extremegradientboostingmodelwithsmoteforheartdiseaseclassification AT adityayogadarmawan extremegradientboostingmodelwithsmoteforheartdiseaseclassification AT dwikaanandaagustinapertiwi extremegradientboostingmodelwithsmoteforheartdiseaseclassification AT jumantounjung extremegradientboostingmodelwithsmoteforheartdiseaseclassification |