Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C
Hepatitis is considered to be one of the most dangerous diseases, which often leads to death if not handled properly. Thus, early detection via precise diagnosis is needed in order to prevent the unfortunate event. This research aims to provide a novel hepatitis C diagnosis based on the machine lea...
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| Format: | Article |
| Language: | Indonesian |
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Indonesian Society of Applied Science (ISAS)
2023-11-01
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| Series: | Journal of Applied Computer Science and Technology |
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| Online Access: | https://journal.isas.or.id/index.php/JACOST/article/view/556 |
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| _version_ | 1849684537751109632 |
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| author | Jimmy Tjen Valentino Pratama |
| author_facet | Jimmy Tjen Valentino Pratama |
| author_sort | Jimmy Tjen |
| collection | DOAJ |
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Hepatitis is considered to be one of the most dangerous diseases, which often leads to death if not handled properly. Thus, early detection via precise diagnosis is needed in order to prevent the unfortunate event. This research aims to provide a novel hepatitis C diagnosis based on the machine learning algorithm, which is the classification tree from the decision tree learning and the distance correlation, which measures the Euclidean distance between 2 vectors. In particular, the goal is to develop a low computational cost yet precise algorithm for diagnosing the possibility of whether a person is being infected with Hepatitis C or not. Based on the experiment, the distance correlation-based classification tree algorithm outperforms the classical classification tree algorithm by around 3% while using only 7 features instead of 12 as in the classical algorithm. Furthermore, the algorithm identified albumin (ALB), Creatinine (CREA), Bilirubin (BIL), Aspartate Transaminase (AST) and Cholesterol (CHOL) as significant risk factors in determining whether someone is potentially infected with hepatitis C or not, with Creatinine is identified as the most important parameter among all 5 parameters mentioned above.
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| format | Article |
| id | doaj-art-017d630b3bea428fb344c8c696fdf147 |
| institution | DOAJ |
| issn | 2723-1453 |
| language | Indonesian |
| publishDate | 2023-11-01 |
| publisher | Indonesian Society of Applied Science (ISAS) |
| record_format | Article |
| series | Journal of Applied Computer Science and Technology |
| spelling | doaj-art-017d630b3bea428fb344c8c696fdf1472025-08-20T03:23:26ZindIndonesian Society of Applied Science (ISAS)Journal of Applied Computer Science and Technology2723-14532023-11-014210.52158/jacost.v4i2.556556Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis CJimmy Tjen0Valentino Pratama1Universitas Widya Dharma PontianakSMA Santo Paulus Pontianak Hepatitis is considered to be one of the most dangerous diseases, which often leads to death if not handled properly. Thus, early detection via precise diagnosis is needed in order to prevent the unfortunate event. This research aims to provide a novel hepatitis C diagnosis based on the machine learning algorithm, which is the classification tree from the decision tree learning and the distance correlation, which measures the Euclidean distance between 2 vectors. In particular, the goal is to develop a low computational cost yet precise algorithm for diagnosing the possibility of whether a person is being infected with Hepatitis C or not. Based on the experiment, the distance correlation-based classification tree algorithm outperforms the classical classification tree algorithm by around 3% while using only 7 features instead of 12 as in the classical algorithm. Furthermore, the algorithm identified albumin (ALB), Creatinine (CREA), Bilirubin (BIL), Aspartate Transaminase (AST) and Cholesterol (CHOL) as significant risk factors in determining whether someone is potentially infected with hepatitis C or not, with Creatinine is identified as the most important parameter among all 5 parameters mentioned above. https://journal.isas.or.id/index.php/JACOST/article/view/556distance correlationHepatitis CDiagnostic pathwayMachine learningClassification tree |
| spellingShingle | Jimmy Tjen Valentino Pratama Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C Journal of Applied Computer Science and Technology distance correlation Hepatitis C Diagnostic pathway Machine learning Classification tree |
| title | Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C |
| title_full | Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C |
| title_fullStr | Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C |
| title_full_unstemmed | Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C |
| title_short | Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C |
| title_sort | penentuan jalur diagnostik penyakit berbasis konsep pembelajaran mesin studi kasus penyakit hepatitis c |
| topic | distance correlation Hepatitis C Diagnostic pathway Machine learning Classification tree |
| url | https://journal.isas.or.id/index.php/JACOST/article/view/556 |
| work_keys_str_mv | AT jimmytjen penentuanjalurdiagnostikpenyakitberbasiskonseppembelajaranmesinstudikasuspenyakithepatitisc AT valentinopratama penentuanjalurdiagnostikpenyakitberbasiskonseppembelajaranmesinstudikasuspenyakithepatitisc |