Support vector machine classification learning algorithm for diabetes prediction
Diabetes is actually one of the primary causes of human mortality. Diabetes is an intense disease affecting various parts of the human body. Diabetes can rise long-range complications including, renal failure and cardiac failure. It is therefore imperative that diabetes be diagnosed in a timely mann...
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| Format: | Article |
| Language: | English |
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Northern Negros State College of Science and Technology (NONESCOST)
2022-07-01
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| Series: | International Research Journal of Science, Technology, Education, and Management |
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| Online Access: | https://irjstem.com/wp-content/uploads/2022/08/IRJSTEM-Volume2_No2_Paper3.pdf |
| _version_ | 1828135545551716352 |
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| author | Renas Rajab Asaad |
| author_facet | Renas Rajab Asaad |
| author_sort | Renas Rajab Asaad |
| collection | DOAJ |
| description | Diabetes is actually one of the primary causes of human mortality. Diabetes is an intense disease affecting various parts of the human body. Diabetes can rise long-range complications including, renal failure and cardiac failure. It is therefore imperative that diabetes be diagnosed in a timely manner to people all over the world. This study develops a method for diabetic classification using machine learning techniques. In this study, Support Vector Machine (SVM) is employed to classify the diabetic disease into two classes based on its different functions, namely, linear, polynomial, and sigmoid functions. The evaluation performance of this study is performed before and after applying the pre-processing stage using different standard criteria. The higher results were obtained by polynomial function 83.77% for accuracy, 86.07% for sensitivity, and 81.97% for specificity. Finally, a comparison between this study and some of the previous studies was addressed, based on the comparison it is shown that this study has a better ability to classify diabetic disease than previous studies. |
| first_indexed | 2024-04-11T17:48:25Z |
| format | Article |
| id | doaj.art-222c199ebb414661abffabc41e970d58 |
| institution | Directory Open Access Journal |
| issn | 2799-063X 2799-0648 |
| language | English |
| last_indexed | 2024-04-11T17:48:25Z |
| publishDate | 2022-07-01 |
| publisher | Northern Negros State College of Science and Technology (NONESCOST) |
| record_format | Article |
| series | International Research Journal of Science, Technology, Education, and Management |
| spelling | doaj.art-222c199ebb414661abffabc41e970d582022-12-22T04:11:12ZengNorthern Negros State College of Science and Technology (NONESCOST)International Research Journal of Science, Technology, Education, and Management2799-063X2799-06482022-07-0122263410.5281/zenodo.6975670Support vector machine classification learning algorithm for diabetes predictionRenas Rajab Asaad0Department of Computer Science, College of Science, Nawroz University, Kurdistan-Region, IraqDiabetes is actually one of the primary causes of human mortality. Diabetes is an intense disease affecting various parts of the human body. Diabetes can rise long-range complications including, renal failure and cardiac failure. It is therefore imperative that diabetes be diagnosed in a timely manner to people all over the world. This study develops a method for diabetic classification using machine learning techniques. In this study, Support Vector Machine (SVM) is employed to classify the diabetic disease into two classes based on its different functions, namely, linear, polynomial, and sigmoid functions. The evaluation performance of this study is performed before and after applying the pre-processing stage using different standard criteria. The higher results were obtained by polynomial function 83.77% for accuracy, 86.07% for sensitivity, and 81.97% for specificity. Finally, a comparison between this study and some of the previous studies was addressed, based on the comparison it is shown that this study has a better ability to classify diabetic disease than previous studies.https://irjstem.com/wp-content/uploads/2022/08/IRJSTEM-Volume2_No2_Paper3.pdfclassificationdata analysisdiabeticfeature extractionmachine learningpima datasetpre-processingsvm technique |
| spellingShingle | Renas Rajab Asaad Support vector machine classification learning algorithm for diabetes prediction International Research Journal of Science, Technology, Education, and Management classification data analysis diabetic feature extraction machine learning pima dataset pre-processing svm technique |
| title | Support vector machine classification learning algorithm for diabetes prediction |
| title_full | Support vector machine classification learning algorithm for diabetes prediction |
| title_fullStr | Support vector machine classification learning algorithm for diabetes prediction |
| title_full_unstemmed | Support vector machine classification learning algorithm for diabetes prediction |
| title_short | Support vector machine classification learning algorithm for diabetes prediction |
| title_sort | support vector machine classification learning algorithm for diabetes prediction |
| topic | classification data analysis diabetic feature extraction machine learning pima dataset pre-processing svm technique |
| url | https://irjstem.com/wp-content/uploads/2022/08/IRJSTEM-Volume2_No2_Paper3.pdf |
| work_keys_str_mv | AT renasrajabasaad supportvectormachineclassificationlearningalgorithmfordiabetesprediction |