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Applying the Temporal Abstraction Technique to the Prediction of Chronic Kidney Disease Progression
Li-Chen Cheng1, Ya-Han Hu2, Shr-Han Chiou3
1Department of Computer Science and Information Management, Soochow University, Taipei, 100, Taiwan.
Insights
This study developed prediction models for chronic kidney disease (CKD) progression using temporal abstraction and data mining. The AdaBoost+CART model accurately predicted end-stage renal disease (ESRD) in CKD patients, aiding clinical decisions.
Area of Science:
- Nephrology
- Data Science
- Public Health
Background:
- Chronic kidney disease (CKD) is a significant public health concern.
- Identifying factors influencing CKD progression is crucial for patient management.
- High-dimensional time-series data from CKD patients present analytical challenges.
Purpose of the Study:
- To develop prediction models for stage 4 CKD patients.
- To predict the progression to end-stage renal disease (ESRD) within six months.
- To evaluate time-related features for predicting CKD deterioration.
Main Methods:
- Integrated temporal abstraction (TA) technique with data mining methods.
- Extracted TA-related features from high-dimensional time-series data.
- Applied C4.5, CART, SVM, and AdaBoost for model development.
Main Results:
- Incorporating temporal information improved prediction model efficiency.
- The AdaBoost+CART model achieved the highest accuracy (0.662) and AUC (0.715).
- Identified TA-related features associated with renal function deterioration.
Conclusions:
- TA-related features provide clinical insights into CKD progression.
- Early ESRD diagnosis is facilitated by tracking temporal changes in lab values.
- Developed models can assist clinicians in decision-making for improved CKD patient care.
Abstract:
Chronic kidney disease (CKD) has attracted considerable attention in the public health domain in recent years. Researchers have exerted considerable effort in attempting to identify critical factors that may affect the deterioration of CKD. In clinical practice, the physical conditions of CKD patients are regularly recorded. The data of CKD patients are recorded as a high-dimensional time-series. Therefore, how to analyze these time-series data for identifying the factors affecting CKD deterioration becomes an interesting topic. This study aims at developing prediction models for stage 4 CKD patients to determine whether their eGFR level decreased to less than 15 ml/min/1.73m2 (end-stage renal disease, ESRD) 6 months after collecting their final laboratory test information by evaluating time-related features. A total of 463 CKD patients collected from January 2004 to December 2013 at one of the biggest dialysis centers in southern Taiwan were included in the experimental evaluation. We integrated the temporal abstraction (TA) technique with data mining methods to develop CKD progression prediction models. Specifically, the TA technique was used to extract vital features (TA-related features) from high-dimensional time-series data, after which several data mining techniques, including C4.5, classification and regression tree (CART), support vector machine, and adaptive boosting (AdaBoost), were applied to develop CKD progression prediction models. The results revealed that incorporating temporal information into the prediction models increased the efficiency of the models. The AdaBoost+CART model exhibited the most accurate prediction among the constructed models (Accuracy: 0.662, Sensitivity: 0.620, Specificity: 0.704, and AUC: 0.715). A number of TA-related features were found to be associated with the deterioration of renal function. These features can provide further clinical information to explain the progression of CKD. TA-related features extracted by long-term tracking of changes in laboratory test values can enable early diagnosis of ESRD. The developed models using these features can facilitate medical personnel in making clinical decisions to provide appropriate diagnoses and improved care quality to patients with CKD.