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

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Authors

  • Sumanta Ray
     Department of Computer Science, The University of Burdwan, Golapbag, Burdwan-713104 ,IN
  • Abhoy Chand Mondal
     Department of Computer Science, The University of Burdwan, Golapbag, Burdwan-713104 ,IN
  • Amartya Neogi
     Department of Computer & Information Science, Dr. B. C. Roy Engineering College, Dugrapur-713206 ,IN
  • Kaberi Dey
     Department of Computer Science, Bagnan College, W.B. ,IN

DOI:

https://doi.org/10.24906/isc/2017/v31/i4/158408

Keywords:

Classification, Essential Hypertension, Random Forests Classifier, Confusion Matrix.

Abstract

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.

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Published

2017-07-01

How to Cite

Ray, S., Mondal, A. C., Neogi, A., & Dey, K. (2017). A Study to Evaluate Symptoms in Essential Hypertension Using Random Forest Decision Tree Algorithm. Indian Science Cruiser, 31(4), 28–35. https://doi.org/10.24906/isc/2017/v31/i4/158408

Issue

Volume 31, Issue 4, July 2017

Section

Search & Survey
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http://journal.r-project.org/archive/2009-2/RJournal_2009-2_Williams.pdf