Using the Random Forest Algorithm for Searching Behavior Patterns in Electronic Health Records

The search for information associated with qualitative data is usually done using data mining algorithms, the presented research analyzes data of patients with essential hypertension (HTA), patients who have developed hypertension but there is no clear reason why it has occurred. In this research, a search of behavioral patterns was performed in the data associated with the clinical records of 8470 patients using the Random Forest algorithm. As a case study, the proposal focuses on finding the relationship between the different pathologies or factors associated to Hypertensive patients (other diseases for example). The findings validate the right use of the algorithm due to the results obtained agrees with the knowledge defined and validated in the literature. Thus, trivial knowledge can be obtained with the algorithm used. However, non-trivial knowledge was also obtained given the analysis performed on a total of 4408 data of female patients and 4062 of male patients showed a great difference between the factors or pathologies that a patient presents when classified according to their sex, thus another deep study must be carried out closely with experts in the area of the health as future research.