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Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives
Khalid Twarish Alhamazani1, Jalawi Alshudukhi1, Saud Aljaloud1
1University of Ha'il, College of Computer Science and Engineering, Department of Computer Science, Ha'il, Saudi Arabia.
Insights
Early detection of chronic kidney disease (CKD) is crucial. Machine learning models, particularly decision forests, show high accuracy (92%) in diagnosing CKD, aiding timely patient treatment and slowing disease progression.
Area of Science:
- Nephrology
- Data Science
- Artificial Intelligence
Background:
- Chronic kidney disease (CKD) is a significant global health concern with high morbidity and mortality.
- Early-stage CKD often lacks visible symptoms, leading to delayed diagnosis and treatment.
- Timely intervention is essential to slow CKD progression.
Purpose of the Study:
- To propose and evaluate a machine learning methodology for the early diagnosis of chronic kidney disease.
- To assess the performance of different AI algorithms in CKD detection.
Main Methods:
- Utilized the Cross Industry Standard Process for Data Mining (CRISP-DM®) framework.
- Data processing and analysis were conducted on the Azure cloud platform.
- Employed the SMOTE technique for balancing the dataset and evaluated four AI algorithms: logistic regression, decision forest, neural network, and jungle of decisions.
Main Results:
- The decision forest algorithm achieved the highest diagnostic accuracy at 92%.
- This performance indicates the potential of machine learning for effective CKD diagnosis.
- The study provides a strong baseline for developing production-ready CKD diagnostic solutions.
Conclusions:
- Machine learning models can significantly aid physicians in the early and accurate diagnosis of CKD.
- The decision forest model demonstrates superior performance for CKD detection.
- This approach offers a promising pathway for improving patient outcomes in chronic kidney disease management.
Abstract:
Chronic kidney disease (CKD) is a global health issue with a high rate of morbidity and mortality and a high rate of disease progression. Because there are no visible symptoms in the early stages of CKD, patients frequently go unnoticed. The early detection of CKD allows patients to receive timely treatment, slowing the disease's progression. Due to its rapid recognition performance and accuracy, machine learning models can effectively assist physicians in achieving this goal. We propose a machine learning methodology for the CKD diagnosis in this paper. This information was completely anonymized. As a reference, the CRISP-DM® model (Cross industry standard process for data mining) was used. The data were processed in its entirety in the cloud on the Azure platform, where the sample data was unbalanced. Then the processes for exploration and analysis were carried out. According to what we have learned, the data were balanced using the SMOTE technique. Four matching algorithms were used after the data balancing was completed successfully. Artificial intelligence (AI) (logistic regression, decision forest, neural network, and jungle of decisions). The decision forest outperformed the other machine learning models with a score of 92%, indicating that the approach used in this study provides a good baseline for solutions in the production.
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