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


Affiliations
1 Department of Computer Science, The University of Burdwan, Golapbag, Burdwan-713104, India
2 Department of Computer & Information Science, Dr. B. C. Roy Engineering College, Dugrapur-713206, India
3 Department of Computer Science, Bagnan College, W.B., India
     

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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.

Keywords

Classification, Essential Hypertension, Random Forests Classifier, Confusion Matrix.
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  • A Study to Evaluate Symptoms in Essential Hypertension Using Random Forest Decision Tree Algorithm

Abstract Views: 590  |  PDF Views: 3

Authors

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

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.

Keywords


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

References





DOI: https://doi.org/10.24906/isc%2F2017%2Fv31%2Fi4%2F158408