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A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease
Sarah A Ebiaredoh-Mienye1, Theo G Swart1, Ebenezer Esenogho1
1Center for Telecommunications, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa.
Insights
This study introduces a machine learning (ML) approach for early chronic kidney disease (CKD) detection. Combining feature selection with a cost-sensitive AdaBoost classifier achieved 99.8% accuracy, aiding timely clinical intervention.
Area of Science:
- Medical Informatics
- Machine Learning in Healthcare
- Public Health
Background:
- Chronic kidney disease (CKD) presents a significant global health challenge with high mortality rates, particularly in developing nations.
- Early-stage CKD often lacks discernible symptoms, hindering timely diagnosis and intervention.
- Effective early detection is crucial for mitigating disease progression and improving patient outcomes.
Purpose of the Study:
- To develop an efficient and cost-effective computer-aided diagnosis system for early CKD detection using machine learning.
- To enhance the accuracy and reduce the resource requirements for CKD screening.
- To propose a novel approach combining feature selection and a cost-sensitive classifier for improved CKD prediction.
Main Methods:
- An information-gain-based feature selection technique was employed to identify key clinical attributes for CKD diagnosis.
- A cost-sensitive Adaptive Boosting (AdaBoost) classifier was developed and trained on the reduced feature set.
- The proposed method was benchmarked against existing CKD prediction techniques and other classifiers.
Main Results:
- The proposed cost-sensitive AdaBoost model, utilizing a reduced feature set, achieved exceptional classification performance: 99.8% accuracy, 100% sensitivity, and 99.8% specificity.
- Feature selection significantly enhanced the performance of various classifiers, demonstrating its positive impact.
- The developed approach proved effective in CKD diagnosis and shows potential for application in other imbalanced medical datasets.
Conclusions:
- The study successfully developed an effective predictive model for early CKD detection.
- The integration of feature selection with a cost-sensitive AdaBoost classifier offers a promising, efficient, and accurate method for CKD screening.
- This approach has the potential to reduce screening time and costs, and can be adapted for detecting other diseases in imbalanced datasets.
Abstract:
The high prevalence of chronic kidney disease (CKD) is a significant public health concern globally. The condition has a high mortality rate, especially in developing countries. CKD often go undetected since there are no obvious early-stage symptoms. Meanwhile, early detection and on-time clinical intervention are necessary to reduce the disease progression. Machine learning (ML) models can provide an efficient and cost-effective computer-aided diagnosis to assist clinicians in achieving early CKD detection. This research proposed an approach to effectively detect CKD by combining the information-gain-based feature selection technique and a cost-sensitive adaptive boosting (AdaBoost) classifier. An approach like this could save CKD screening time and cost since only a few clinical test attributes would be needed for the diagnosis. The proposed approach was benchmarked against recently proposed CKD prediction methods and well-known classifiers. Among these classifiers, the proposed cost-sensitive AdaBoost trained with the reduced feature set achieved the best classification performance with an accuracy, sensitivity, and specificity of 99.8%, 100%, and 99.8%, respectively. Additionally, the experimental results show that the feature selection positively impacted the performance of the various classifiers. The proposed approach has produced an effective predictive model for CKD diagnosis and could be applied to more imbalanced medical datasets for effective disease detection.
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