Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model
Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable tools for reducing randomness and enhancing cli...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/7/3/144 |
| _version_ | 1797581213249044480 |
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| author | Muhammad Shoaib Arif Aiman Mukheimer Daniyal Asif |
| author_facet | Muhammad Shoaib Arif Aiman Mukheimer Daniyal Asif |
| author_sort | Muhammad Shoaib Arif |
| collection | DOAJ |
| description | Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable tools for reducing randomness and enhancing clinical decision-making. However, conventional methods for CKD detection often lack accuracy due to their reliance on limited sets of biological attributes. This research proposes a novel ML model for predicting CKD, incorporating various preprocessing steps, feature selection, a hyperparameter optimization technique, and ML algorithms. To address challenges in medical datasets, we employ iterative imputation for missing values and a novel sequential approach for data scaling, combining robust scaling, z-standardization, and min-max scaling. Feature selection is performed using the Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 100% accuracy. Our approach, combining innovative preprocessing steps, the Boruta feature selection, and the k-nearest neighbors algorithm, along with a hyperparameter optimization using grid-search cross-validation (CV), demonstrates its effectiveness in enhancing the early detection of CKD. This research highlights the potential of ML techniques in improving clinical support systems and reducing the impact of uncertainty in chronic disorder prognosis. |
| first_indexed | 2024-03-10T23:02:08Z |
| format | Article |
| id | doaj.art-237fd4d0b6cc4446ae903e167fe775ed |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T23:02:08Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-237fd4d0b6cc4446ae903e167fe775ed2023-11-19T09:34:21ZengMDPI AGBig Data and Cognitive Computing2504-22892023-08-017314410.3390/bdcc7030144Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning ModelMuhammad Shoaib Arif0Aiman Mukheimer1Daniyal Asif2Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh 11586, Saudi ArabiaDepartment of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh 11586, Saudi ArabiaDepartment of Mathematics, COMSATS University Islamabad, Park Road, Islamabad 45550, PakistanClinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable tools for reducing randomness and enhancing clinical decision-making. However, conventional methods for CKD detection often lack accuracy due to their reliance on limited sets of biological attributes. This research proposes a novel ML model for predicting CKD, incorporating various preprocessing steps, feature selection, a hyperparameter optimization technique, and ML algorithms. To address challenges in medical datasets, we employ iterative imputation for missing values and a novel sequential approach for data scaling, combining robust scaling, z-standardization, and min-max scaling. Feature selection is performed using the Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 100% accuracy. Our approach, combining innovative preprocessing steps, the Boruta feature selection, and the k-nearest neighbors algorithm, along with a hyperparameter optimization using grid-search cross-validation (CV), demonstrates its effectiveness in enhancing the early detection of CKD. This research highlights the potential of ML techniques in improving clinical support systems and reducing the impact of uncertainty in chronic disorder prognosis.https://www.mdpi.com/2504-2289/7/3/144chronic kidney diseasemachine learningartificial intelligencedata sciencehealthcarebioinformatics |
| spellingShingle | Muhammad Shoaib Arif Aiman Mukheimer Daniyal Asif Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model Big Data and Cognitive Computing chronic kidney disease machine learning artificial intelligence data science healthcare bioinformatics |
| title | Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model |
| title_full | Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model |
| title_fullStr | Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model |
| title_full_unstemmed | Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model |
| title_short | Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model |
| title_sort | enhancing the early detection of chronic kidney disease a robust machine learning model |
| topic | chronic kidney disease machine learning artificial intelligence data science healthcare bioinformatics |
| url | https://www.mdpi.com/2504-2289/7/3/144 |
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