Bheema Lingaiah Thamineni*, Abeba Getachew Asmare, Hanumesh Kumar Dasari and Solomon Woldetsadik
Background: Chronic kidney disease is one of a major global public health issue, affecting over 10% of the population worldwide. It is the leading cause of death in 2016 ranking 16th and is expected to rise to 5th rank by 2040. Consequently, tools to identify patients at high risk of having CKD and management of risk factors are needed, particularly in limited-resources settings where laboratory facilities are scarce. This study aimed to develop a risk prediction and management system using data from JUMC, SPHMMC and MTUTH.
Objective: To develop chronic kidney disease risk prediction and management system is using expert system.
Method: General chronic kidney disease risk factor were collected from expert knowledge. The identified general risk factors were applied on 384 patients data collected from three hospitals to identify risk factors in Ethiopia. The risk factors were identified using statistical analysis. After identifying the risk factors from the statistical analysis, risk factor managements techniques were identified from expert knowledge. Knowledge gained from the expert knowledge and statistical analyses were combined and developed using rule based expert system.
Main outcome measure: Accuracy, precision and recall are the parameters which have been evaluated from the developed system using confusion matrix.
Result: The system has showed 63.3%, 65.3% and 77.5% accuracy at 14%, 24% and 34% cut off percent respectively in estimating probability.
Conclusion: This study will have significance in preventing chronic kidney disease at early stage and creating awareness.
Funding statement: The authors received no specific funding for this study.
Published Date: 2023-09-29; Received Date: 2023-05-09
