Data Mining Techniques for Iraqi Biochemical Dataset Analysis
This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, an...
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| Format: | Article |
| Language: | Arabic |
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College of Science for Women, University of Baghdad
2022-04-01
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| Series: | Baghdad Science Journal |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5407 |
| _version_ | 1818385191939342336 |
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| author | Sarah Sameer Suhad Faisal Behadili |
| author_facet | Sarah Sameer Suhad Faisal Behadili |
| author_sort | Sarah Sameer |
| collection | DOAJ |
| description |
This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB), and Support Vector Machine (SVM) techniques. CART gives clear results with high accuracy between the six supervised algorithms. It is worth noting that the preprocessing steps take remarkable efforts to handle this type of data, since its pure data set has so many null values of a ratio 94.8%, then it becomes 0% after achieving the preprocessing steps. Then, in order to apply CART algorithm, several determined tests were assumed as classes. The decision to select the tests which had been assumed as classes were depending on their acquired accuracy. Consequently, enabling the physicians to trace and connect the tests result with each other, which extends its impact on patients’ health.
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| first_indexed | 2024-12-14T03:34:14Z |
| format | Article |
| id | doaj.art-b680201da6bf4db2bf076d900ef9736a |
| institution | Directory Open Access Journal |
| issn | 2078-8665 2411-7986 |
| language | Arabic |
| last_indexed | 2024-12-14T03:34:14Z |
| publishDate | 2022-04-01 |
| publisher | College of Science for Women, University of Baghdad |
| record_format | Article |
| series | Baghdad Science Journal |
| spelling | doaj.art-b680201da6bf4db2bf076d900ef9736a2022-12-21T23:18:40ZaraCollege of Science for Women, University of BaghdadBaghdad Science Journal2078-86652411-79862022-04-0119210.21123/bsj.2022.19.2.0385Data Mining Techniques for Iraqi Biochemical Dataset AnalysisSarah Sameer0Suhad Faisal Behadili1Computer Science Department, College of Science, University of Baghdad, Baghdad, IraqComputer Science Department, College of Science, University of Baghdad, Baghdad, Iraq This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB), and Support Vector Machine (SVM) techniques. CART gives clear results with high accuracy between the six supervised algorithms. It is worth noting that the preprocessing steps take remarkable efforts to handle this type of data, since its pure data set has so many null values of a ratio 94.8%, then it becomes 0% after achieving the preprocessing steps. Then, in order to apply CART algorithm, several determined tests were assumed as classes. The decision to select the tests which had been assumed as classes were depending on their acquired accuracy. Consequently, enabling the physicians to trace and connect the tests result with each other, which extends its impact on patients’ health. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5407Biomedical, Classification And Regression Tree (CART), Data mining, Hierarchical clustering, K-means. |
| spellingShingle | Sarah Sameer Suhad Faisal Behadili Data Mining Techniques for Iraqi Biochemical Dataset Analysis Baghdad Science Journal Biomedical, Classification And Regression Tree (CART), Data mining, Hierarchical clustering, K-means. |
| title | Data Mining Techniques for Iraqi Biochemical Dataset Analysis |
| title_full | Data Mining Techniques for Iraqi Biochemical Dataset Analysis |
| title_fullStr | Data Mining Techniques for Iraqi Biochemical Dataset Analysis |
| title_full_unstemmed | Data Mining Techniques for Iraqi Biochemical Dataset Analysis |
| title_short | Data Mining Techniques for Iraqi Biochemical Dataset Analysis |
| title_sort | data mining techniques for iraqi biochemical dataset analysis |
| topic | Biomedical, Classification And Regression Tree (CART), Data mining, Hierarchical clustering, K-means. |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5407 |
| work_keys_str_mv | AT sarahsameer dataminingtechniquesforiraqibiochemicaldatasetanalysis AT suhadfaisalbehadili dataminingtechniquesforiraqibiochemicaldatasetanalysis |