Machine Learning Algorithm for Detecting and Predicting Chronic Kidney Disease Article Swipe
Sandeep Sharma
,
Saruchi Saruchi
,
Avneesh Narwal
,
K Meghana
,
Manjeet Singh
,
Rohit Maurya
,
Yash Upadhyay
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.13005/bpj/3165
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.13005/bpj/3165
Chronic kidney disease is a progressive condition that often remains undiagnosed until its later stages due to the absence of noticeable symptoms. Early detection is essential for timely intervention and treatment. Whereas other research has mostly centered on the detection of kidney disease in later stages, this research contributes to the field by combining predictive modeling in order to ascertain disease progression in earlier phases. Through the use of both multi-classification and binary classification methods, this research improves the knowledge of chronic kidney disease progression, enabling specific treatment approaches. Sophisticated machine learning algorithms like K-Nearest Neighbor, Decision Tree, and Random Forest have been used to evaluate the accuracy of disease stage prediction. Comparative analysis of different predictive models indicates their efficiency, resulting in enhanced diagnostic accuracy and efficiency. This study adds value to the health industry through the application of machine learning in the early diagnosis and improved management of diseases. Disease prediction enables clinicians to apply timely interventions, minimize complications, and in the end, decrease morbidity and mortality rates related to kidney disease. All these activities come in line with the world aim of enhancing health outcomes and well-being. The anticipated predictive model evidenced a precision rate of 99.16 percent, outpacing other studies using different machine learning classifiers such as Random Forest Classifier, Ada Boost Classifier, Cat Boost, Stochastic Gradient Boosting, Gradient Boosting Classifier, Extreme Gradient Boosting, K-Nearest Neighbor, Extra Trees Classifier, and Decision Tree Classifier. This paper strengthens the importance of artificial intelligence in promoting the diagnostics of chronic kidney disease as well as outcomes in patient care.
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https://doi.org/10.13005/bpj/3165Digital Object Identifier
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Machine Learning Algorithm for Detecting and Predicting Chronic Kidney DiseaseWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-06-30Full publication date if available
- Authors
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Sandeep Sharma, Saruchi Saruchi, Avneesh Narwal, K Meghana, Manjeet Singh, Rohit Maurya, Yash UpadhyayList of authors in order
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https://doi.org/10.13005/bpj/3165Publisher landing page
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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://doi.org/10.13005/bpj/3165Direct OA link when available
- Concepts
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Kidney disease, Computer science, Machine learning, Artificial intelligence, Algorithm, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Boost | 215 |
| abstract_inverted_index.Early | 22 |
| abstract_inverted_index.Extra | 230 |
| abstract_inverted_index.Tree, | 97 |
| abstract_inverted_index.Trees | 231 |
| abstract_inverted_index.apply | 156 |
| abstract_inverted_index.care. | 259 |
| abstract_inverted_index.early | 144 |
| abstract_inverted_index.field | 51 |
| abstract_inverted_index.later | 13, 44 |
| abstract_inverted_index.model | 193 |
| abstract_inverted_index.often | 8 |
| abstract_inverted_index.order | 57 |
| abstract_inverted_index.other | 32, 202 |
| abstract_inverted_index.paper | 238 |
| abstract_inverted_index.rates | 169 |
| abstract_inverted_index.stage | 110 |
| abstract_inverted_index.study | 129 |
| abstract_inverted_index.their | 119 |
| abstract_inverted_index.these | 175 |
| abstract_inverted_index.until | 11 |
| abstract_inverted_index.using | 204 |
| abstract_inverted_index.value | 131 |
| abstract_inverted_index.world | 182 |
| abstract_inverted_index.Boost, | 218 |
| abstract_inverted_index.Forest | 100, 212 |
| abstract_inverted_index.Random | 99, 211 |
| abstract_inverted_index.binary | 72 |
| abstract_inverted_index.health | 134, 186 |
| abstract_inverted_index.kidney | 1, 41, 82, 172, 251 |
| abstract_inverted_index.models | 117 |
| abstract_inverted_index.mostly | 35 |
| abstract_inverted_index.stages | 14 |
| abstract_inverted_index.timely | 27, 157 |
| abstract_inverted_index.Chronic | 0 |
| abstract_inverted_index.Disease | 151 |
| abstract_inverted_index.Extreme | 225 |
| abstract_inverted_index.Through | 65 |
| abstract_inverted_index.Whereas | 31 |
| abstract_inverted_index.absence | 18 |
| abstract_inverted_index.chronic | 81, 250 |
| abstract_inverted_index.disease | 2, 42, 60, 83, 109, 252 |
| abstract_inverted_index.earlier | 63 |
| abstract_inverted_index.enables | 153 |
| abstract_inverted_index.machine | 90, 140, 206 |
| abstract_inverted_index.patient | 258 |
| abstract_inverted_index.phases. | 64 |
| abstract_inverted_index.related | 170 |
| abstract_inverted_index.remains | 9 |
| abstract_inverted_index.stages, | 45 |
| abstract_inverted_index.studies | 203 |
| abstract_inverted_index.through | 136 |
| abstract_inverted_index.Boosting | 223 |
| abstract_inverted_index.Decision | 96, 234 |
| abstract_inverted_index.Gradient | 220, 222, 226 |
| abstract_inverted_index.accuracy | 107, 125 |
| abstract_inverted_index.analysis | 113 |
| abstract_inverted_index.centered | 36 |
| abstract_inverted_index.decrease | 165 |
| abstract_inverted_index.disease. | 173 |
| abstract_inverted_index.enabling | 85 |
| abstract_inverted_index.enhanced | 123 |
| abstract_inverted_index.evaluate | 105 |
| abstract_inverted_index.improved | 147 |
| abstract_inverted_index.improves | 77 |
| abstract_inverted_index.industry | 135 |
| abstract_inverted_index.learning | 91, 141, 207 |
| abstract_inverted_index.methods, | 74 |
| abstract_inverted_index.minimize | 159 |
| abstract_inverted_index.modeling | 55 |
| abstract_inverted_index.outcomes | 187, 256 |
| abstract_inverted_index.percent, | 200 |
| abstract_inverted_index.research | 33, 47, 76 |
| abstract_inverted_index.specific | 86 |
| abstract_inverted_index.Boosting, | 221, 227 |
| abstract_inverted_index.K-Nearest | 94, 228 |
| abstract_inverted_index.Neighbor, | 95, 229 |
| abstract_inverted_index.ascertain | 59 |
| abstract_inverted_index.combining | 53 |
| abstract_inverted_index.condition | 6 |
| abstract_inverted_index.detection | 23, 39 |
| abstract_inverted_index.diagnosis | 145 |
| abstract_inverted_index.different | 115, 205 |
| abstract_inverted_index.diseases. | 150 |
| abstract_inverted_index.enhancing | 185 |
| abstract_inverted_index.essential | 25 |
| abstract_inverted_index.evidenced | 194 |
| abstract_inverted_index.indicates | 118 |
| abstract_inverted_index.knowledge | 79 |
| abstract_inverted_index.morbidity | 166 |
| abstract_inverted_index.mortality | 168 |
| abstract_inverted_index.outpacing | 201 |
| abstract_inverted_index.precision | 196 |
| abstract_inverted_index.promoting | 246 |
| abstract_inverted_index.resulting | 121 |
| abstract_inverted_index.symptoms. | 21 |
| abstract_inverted_index.treatment | 87 |
| abstract_inverted_index.Stochastic | 219 |
| abstract_inverted_index.activities | 176 |
| abstract_inverted_index.algorithms | 92 |
| abstract_inverted_index.artificial | 243 |
| abstract_inverted_index.clinicians | 154 |
| abstract_inverted_index.diagnostic | 124 |
| abstract_inverted_index.importance | 241 |
| abstract_inverted_index.management | 148 |
| abstract_inverted_index.noticeable | 20 |
| abstract_inverted_index.prediction | 152 |
| abstract_inverted_index.predictive | 54, 116, 192 |
| abstract_inverted_index.treatment. | 30 |
| abstract_inverted_index.Classifier, | 213, 216, 224, 232 |
| abstract_inverted_index.Classifier. | 236 |
| abstract_inverted_index.Comparative | 112 |
| abstract_inverted_index.anticipated | 191 |
| abstract_inverted_index.application | 138 |
| abstract_inverted_index.approaches. | 88 |
| abstract_inverted_index.classifiers | 208 |
| abstract_inverted_index.contributes | 48 |
| abstract_inverted_index.diagnostics | 248 |
| abstract_inverted_index.efficiency, | 120 |
| abstract_inverted_index.efficiency. | 127 |
| abstract_inverted_index.prediction. | 111 |
| abstract_inverted_index.progression | 61 |
| abstract_inverted_index.progressive | 5 |
| abstract_inverted_index.strengthens | 239 |
| abstract_inverted_index.undiagnosed | 10 |
| abstract_inverted_index.well-being. | 189 |
| abstract_inverted_index.intelligence | 244 |
| abstract_inverted_index.intervention | 28 |
| abstract_inverted_index.progression, | 84 |
| abstract_inverted_index.Sophisticated | 89 |
| abstract_inverted_index.classification | 73 |
| abstract_inverted_index.complications, | 160 |
| abstract_inverted_index.interventions, | 158 |
| abstract_inverted_index.multi-classification | 70 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.37776324 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |