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Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in
Md Rashed-Al-Mahfuz1, Abedul Haque2, Akm Azad3
1Department of Computer Science and EngineeringUniversity of RajshahiRajshahi6205Bangladesh.
Insights
Machine learning models accurately diagnose chronic kidney disease (CKD) early using vital parameters. This approach reduces costs and improves patient outcomes for timely treatment.
Area of Science:
- Nephrology
- Medical Informatics
- Artificial Intelligence
Background:
- Chronic kidney disease (CKD) presents a significant global health challenge, marked by high morbidity and mortality rates.
- Late-stage diagnosis and limited testing infrastructure exacerbate CKD patient outcomes, especially in developing nations.
- Affordable computer-aided diagnosis leveraging vital parameter analytics offers a pathway to reduce costs and enhance early detection.
Purpose of the Study:
- To develop machine learning models for accurate early diagnosis of CKD.
- To identify key pathological categories and clinical test attributes for cost-effective diagnostic screening.
- To evaluate classifier performance on optimized datasets using selected clinical attributes.
Main Methods:
- Development of machine learning models utilizing selective key pathological categories.
- Identification and optimization of clinical test attributes for CKD diagnosis.
- Evaluation of various classifiers, including random forest, using k-fold cross-validation on optimized datasets.
- Focus on low-cost urine, blood, and clinical parameters.
Main Results:
- Optimized datasets with key attributes demonstrated strong performance in CKD diagnosis.
- Machine learning models achieved high accuracy in identifying CKD.
- The random forest classifier exhibited the best performance among evaluated models.
- Cost-effective clinical test attributes were successfully utilized.
Conclusions:
- The proposed machine learning approach provides effective predictive analytics for CKD screening.
- This method can be developed into a valuable resource for improved and timely CKD diagnosis and treatment.
- Early detection through advanced analytics can significantly enhance patient management and outcomes.
Objective:
Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes.
Methods:
In this study, we developed machine learning models using selective key pathological categories to identify clinical test attributes that will aid in accurate early diagnosis of CKD. Such an approach will save time and costs for diagnostic screening. We have also evaluated the performance of several classifiers with k-fold cross-validation on optimized datasets derived using these selected clinical test attributes.
Results:
Our results suggest that the optimized datasets with important attributes perform well in diagnosis of CKD using our proposed machine learning models. Furthermore, we evaluated clinical test attributes based on urine and blood tests along with clinical parameters that have low costs of acquisition. The predictive models with the optimized and pathologically categorized attributes set yielded high levels of CKD diagnosis accuracy with random forest (RF) classifier being the best performing.
Conclusions:
Our machine learning approach has yielded effective predictive analytics for CKD screening which can be developed as a resource to facilitate improved CKD screening for enhanced and timely treatment plans.
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