## 5 Clinical Practice Context  
Using eHealth and mHealth systems to aid in the treatment and identification of chronic diseases can be one way to reduce high mortality rates through monitoring chronic diseases such as CKD. This situation refers to using information and technologies intelligently and effectively to guide those whom public health systems will eventually assist. Early computer-aided identification of CKD can help people living in the countryside and environments with difficult access to primary care. In addition, mobile health apps (i.e., mHealth), which generate personal health records (PHR), can be used to reduce issues (i.e., store a patient's complete medical history with diagnosis, administered medications, plans for treatment, vaccination dates, allergies) related to primary health care in remote locations. Therefore, the presented classification models can be used to develop eHealth and mHealth systems that assist patients, clinicians, and the government in monitoring CKD and its risk factors. Using the Brazilian CKD dataset, we recommend applying the DT model with data resampled with the SMOTE technique to develop a DSS. The DT model achieved high performance, and it is considered a white box analysis approach with a straightforward interpretation of results. Interpreting the results helps doctors understand how the model achieved a specific risk rating, increasing these professionals' confidence in the results. Figure 4: ROC curves of the DT model using SMOTE and the nested CV method for the sixth, seventh, eighth, ninth, and tenth folds. Each graphic represents one of the ten folds. The ML model can be the basis for developing a DSS to identify and monitor CKD in Brazilian communities, where the interaction between three actors is proposed: doctor, patient, and public health system (Figure 5). The system used by patients is presented as a web-based system divided into front-end and back-end, which contains PHR and CKD risk assessment functionality. The risk assessment is performed after inputting the results of exams, where the classification of risk of CKD is based on the DT model. After the user's clinical evaluation, the system can send a clinical document, structured from the HL7 clinical document architecture (CDA) to the doctor responsible for monitoring the patient. The HL7 CDA document is an XML file that contains the risk analysis data, a risk analysis DT, and the PHR. The medical system receives the CDA document to confirm the risk assessment by analyzing the classification, the DT, and the PHR data. In an uncertain diagnosis, the doctor can send the CDA document to other doctors for a second opinion. The patient and medical subsystems use web services provided by the Server subsystem to update the PHR of patients as part of the medical records available at a healthcare facility. We provide a more detailed explanation of this type of system for CKD and related technologies in our previous publication [28]. Therefore, we implemented a web-based application considering the system used by patients, as an improvement of the results presented in [28]. The back-end of such subsystem was implemented using the Java programming language and web services. The subsystem comprises the following main features: access control, management of ingested drugs, management of allergies, management of examinations, monitoring of hypertension and DM, execution of risk analysis, generation and sharing CDA documents, and analysis of the emergency. In contrast, the front-end of the subsystem is implemented using HTML 5, Bootstrap, JavaScript, and Vue.js. For the graphical user interface (GUI) for recording a new CKD test result (the main inputs for the risk assessment model), the user can also upload an XML file containing the test results to present a large number of manual inputs. Once the patient provides the current test results, the main GUI of the subsystem is updated, showing the test results available for the risk assessment.