Performance Comparison of Machine Learning Algorithms for Classification of Chronic Kidney Disease (CKD) Article Swipe
Azian Azamimi Abdullah
,
Syazwani Adli Hafidz
,
Wan Khairunizam
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1529/5/052077
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1529/5/052077
Kidney is one of the vital organs in a human body while ironically, chronic kidney disease (CKD) is one of the main causes of death in the world. Due to the low rate of loss of kidney function, the disease is often overlooked until it is in a really bad condition. Dysfunctional kidney may lead to accumulation of wastes in blood which would affect several other systems and functions of the body such as blood pressure, red blood cell production, vitamin D and bone health. Machine learning algorithms can help in classifying the patients who have CKD or not. Even though several studies have been made to classify CKD on patients using machine-learning tool, not many researchers perform pre-processing and feature selection technique to obtain quality and dependable result. Machine learning used with feature selection techniques are shown to have better and more dependable result. In this study, feature selection methods such as Random Forest feature selection, forward selection, forward exhaustive selection, backward selection and backward exhaustive selection were identified and evaluated. Then, machine learning classifiers such as Random Forest, Linear and Radial SVM, Naïve Bayes and Logistic Regression were implemented. Lastly, the performance of each machine-learning model was evaluated in terms of accuracy, sensitivity, specificity and AUC score. The results showed that Random Forest classifier with Random Forest feature selection is the most suitable machine learning model for classification of CKD as it has the highest accuracy, sensitivity, specificity and AUC with 98.825%, 98.04%, 100% and 98.9% respectively which outperformed other classifiers.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/1529/5/052077
- https://iopscience.iop.org/article/10.1088/1742-6596/1529/5/052077/pdf
- OA Status
- diamond
- Cited By
- 19
- References
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3036202055
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3036202055Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/1529/5/052077Digital Object Identifier
- Title
-
Performance Comparison of Machine Learning Algorithms for Classification of Chronic Kidney Disease (CKD)Work title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-01Full publication date if available
- Authors
-
Azian Azamimi Abdullah, Syazwani Adli Hafidz, Wan KhairunizamList of authors in order
- Landing page
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https://doi.org/10.1088/1742-6596/1529/5/052077Publisher landing page
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https://iopscience.iop.org/article/10.1088/1742-6596/1529/5/052077/pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/1742-6596/1529/5/052077/pdfDirect OA link when available
- Concepts
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Random forest, Machine learning, Feature selection, Artificial intelligence, Naive Bayes classifier, Support vector machine, Kidney disease, Computer science, Logistic regression, Algorithm, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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19Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 3, 2023: 6, 2022: 5, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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7Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.while | 12 |
| abstract_inverted_index.would | 63 |
| abstract_inverted_index.Forest | 155, 215, 219 |
| abstract_inverted_index.Kidney | 1 |
| abstract_inverted_index.Linear | 181 |
| abstract_inverted_index.Naïve | 185 |
| abstract_inverted_index.Radial | 183 |
| abstract_inverted_index.Random | 154, 179, 214, 218 |
| abstract_inverted_index.affect | 64 |
| abstract_inverted_index.better | 141 |
| abstract_inverted_index.causes | 23 |
| abstract_inverted_index.kidney | 15, 37, 53 |
| abstract_inverted_index.obtain | 125 |
| abstract_inverted_index.organs | 7 |
| abstract_inverted_index.really | 49 |
| abstract_inverted_index.score. | 209 |
| abstract_inverted_index.showed | 212 |
| abstract_inverted_index.study, | 148 |
| abstract_inverted_index.though | 101 |
| abstract_inverted_index.wastes | 59 |
| abstract_inverted_index.world. | 28 |
| abstract_inverted_index.98.04%, | 245 |
| abstract_inverted_index.Forest, | 180 |
| abstract_inverted_index.Lastly, | 192 |
| abstract_inverted_index.Machine | 86, 130 |
| abstract_inverted_index.chronic | 14 |
| abstract_inverted_index.disease | 16, 40 |
| abstract_inverted_index.feature | 121, 134, 149, 156, 220 |
| abstract_inverted_index.forward | 158, 160 |
| abstract_inverted_index.health. | 85 |
| abstract_inverted_index.highest | 237 |
| abstract_inverted_index.machine | 174, 226 |
| abstract_inverted_index.methods | 151 |
| abstract_inverted_index.perform | 118 |
| abstract_inverted_index.quality | 126 |
| abstract_inverted_index.result. | 129, 145 |
| abstract_inverted_index.results | 211 |
| abstract_inverted_index.several | 65, 102 |
| abstract_inverted_index.studies | 103 |
| abstract_inverted_index.systems | 67 |
| abstract_inverted_index.vitamin | 81 |
| abstract_inverted_index.98.825%, | 244 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Logistic | 188 |
| abstract_inverted_index.backward | 163, 166 |
| abstract_inverted_index.classify | 108 |
| abstract_inverted_index.learning | 87, 131, 175, 227 |
| abstract_inverted_index.patients | 94, 111 |
| abstract_inverted_index.suitable | 225 |
| abstract_inverted_index.accuracy, | 204, 238 |
| abstract_inverted_index.evaluated | 200 |
| abstract_inverted_index.function, | 38 |
| abstract_inverted_index.functions | 69 |
| abstract_inverted_index.pressure, | 76 |
| abstract_inverted_index.selection | 122, 135, 150, 164, 168, 221 |
| abstract_inverted_index.technique | 123 |
| abstract_inverted_index.Regression | 189 |
| abstract_inverted_index.algorithms | 88 |
| abstract_inverted_index.classifier | 216 |
| abstract_inverted_index.condition. | 51 |
| abstract_inverted_index.dependable | 128, 144 |
| abstract_inverted_index.evaluated. | 172 |
| abstract_inverted_index.exhaustive | 161, 167 |
| abstract_inverted_index.identified | 170 |
| abstract_inverted_index.overlooked | 43 |
| abstract_inverted_index.selection, | 157, 159, 162 |
| abstract_inverted_index.techniques | 136 |
| abstract_inverted_index.classifiers | 176 |
| abstract_inverted_index.classifying | 92 |
| abstract_inverted_index.ironically, | 13 |
| abstract_inverted_index.performance | 194 |
| abstract_inverted_index.production, | 80 |
| abstract_inverted_index.researchers | 117 |
| abstract_inverted_index.specificity | 206, 240 |
| abstract_inverted_index.accumulation | 57 |
| abstract_inverted_index.classifiers. | 253 |
| abstract_inverted_index.implemented. | 191 |
| abstract_inverted_index.outperformed | 251 |
| abstract_inverted_index.respectively | 249 |
| abstract_inverted_index.sensitivity, | 205, 239 |
| abstract_inverted_index.Dysfunctional | 52 |
| abstract_inverted_index.classification | 230 |
| abstract_inverted_index.pre-processing | 119 |
| abstract_inverted_index.machine-learning | 113, 197 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.9392985 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |