Following recent advances in wearable devices and AI classifier models, a system using the CatBoost classifier model to analyze data provided by Smartwatches and cellular devices through remote monitoring system was proposed, in order to improve the accuracy of making the decision in such systems. The input data for each participant were consisted of the patient's medical history along with the patient's vital signals, and statistical features extracted from the signal time series. Vital signals were collected mainly using smartwatches. The model performed binary classification (N=49) across a dataset split into 3 folds, using cross-validation. The Optuna algorithm was used to optimize the model. It scored (91.88 ± 7.40)% balanced accuracy, (83.81 ± 3.30)% F1-score and with (95.18 ± 6.27)% ROC-AUC. Overall, the system showed promising results towards classifying high/low risk patients, given the low number of samples and high evaluation scores. Possible improvements in the project include a higher number of samples and model calibration to enhance the reliability of risk scores.