## 2 Related Work
### Application of Learning Techniques for HBP  
To improve the management of hypertensive patients, the literature has focused in recent years on the application of learning techniques. One relevant scenario has been the identification of features that can negatively impact this disease. In this sense, [PERSON] et al. [12] studied the Systolic Blood Pressure Intervention Trial (SPRINT) dataset containing electronic health records, including a wide variety of characteristics in categories such as demographics, medication, or laboratory parameters.  
Other works in the literature have focused on the prediction of the disease in the future. [PERSON] et al. [13] used the XGBoost algorithm over data from electronic health records to predict the risk of developing hypertension in the next year. These data contained features from categories such as medication history or socioeconomic features. They highlighted the enormous impact that the presence of other chronic conditions has over the appearance of HBP. In addition, in this line, [PERSON] et al. [14] determined if a person was prone to develop HBP in the future, considering the following features: age, anger, anxiety, obesity, and cholesterol level. For that, they used multiple machine learning (ML) algorithms, where Random Forest obtained the best results. Moreover, [PERSON] et al. [15] applied neural networks over a medical dataset to predict the risk of developing HBP, using features such as age, blood pressure values, laboratory analysis, and smoking and exercise habits. Finally, [PERSON] et al. [16] applied logistic regression considering as features the gender, race, BMI, age, smoking habits, and previous diseases such as kidney disease and diabetes.  
Other works have also studied how to predict blood pressure values. [PERSON] et al. [17] implemented a smartphone application able to pair with a wireless blood pressure monitor, using multiple ML algorithms to predict the value of the blood pressure. They highlighted that the literature only focused on medical data to perform the predictions, not using contextual features. Because of that, they included features such as the altitude or geographical coordinates. This work documented a Root Mean Squared Error (RMSE) between 4 and 5.5 points for the diastolic pressure and between 7 and 9 for the diastolic pressure. The differences between the algorithms tested were not significant.  
[PERSON] et al. [18] defined the first work using a wearable to acquire domain-relevant data, combined with a Bluetooth pressure monitor. This work performed predictions of blood pressure one day ahead using features extracted from the exercise and sleep data captured by the wearable. This work used multiple ML algorithms to calculate the predictions, Random Forest being the method offering better results. The Mean Absolute Error (MAE) ranged between 5 and 9 points among the different algorithms tested.  
Finally, Table 1 presents a summary of the features used by each publication studied in this section. It also indicates that the present work partially studies health records. Although we do not have access to users' health records, we do acquire data concerning their current physical and mental status using surveys.