Background: Chronic Kidney Disease (CKD) is a worldwide health problem, usually diagnosed in late stages of the disease, increasing public health costs and mortality rates. The late diagnosis is even more critical in developing countries due to the high levels of poverty, a large number of hard-to-reach locations, and sometimes lack/precarious primary care.
Methods: We designed and evaluated an intelligent web-based Decision Support System (DSS) using the J48 decision tree machine learning algorithm, knowledge-based system concepts, the clinical document architecture, Cohen's kappa statistic, and interviews with an experienced nephrologist.
Results: We provided a DSS methodology, that guided the development of the system to assist patients, primary care physicians, and the government in identifying and monitoring the CKD in Brazilian communities. The system provides remote monitoring features. A CKD dataset enabled the evaluation of the J48 decision tree algorithm, while Cohen's kappa statistic guided the evaluation of the knowledge-based system by interviews with an experienced nephrologist.
Conclusion: The DSS facilitates the identification and monitoring of the CKD considering low-income populations in Brazil. In addition, the methodology and DSS can be re-used in other developing countries with similar scenarios.
Trial registration: 47350313.9.0000.5013.