A Study to Evaluate Symptoms in Essential Hypertension Using Random Forest Decision Tree Algorithm

In the present study, we would like to gain the insight of the medical data through classification based data mining technique, namely random forests classification. The paper presents a hypertension risk factor symptom classification task where the decisions should be made only on the basis of general information and basis biochemical data. Even though advancements in the field of medicine make it easier to treat hypertension, there are still insufficiencies regarding the determination and evaluation of its risk factors. In this study, various risk factors used to diagnose were investigated by taking into consideration the individuals with common symptoms and complaints. Patient data were collected from a homeopathic medical practitioner. Present analysis predicts that Hypertrophy of Heart and allied, Stiffness of neck and Sensitivity to noise are most important risk symptom to predict hypertension.