Research Article Open Access

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

Bindushree Doddasiddavanahalli Channabasavaraju1 and Udayarani Vinayakamurthy1
  • 1 REVA University, India
Journal of Computer Science
Volume 16 No. 1, 2020, 105-116

DOI: https://doi.org/10.3844/jcssp.2020.105.116

Submitted On: 30 September 2019 Published On: 27 January 2020

How to Cite: Channabasavaraju, B. D. & Vinayakamurthy, U. (2020). An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest. Journal of Computer Science, 16(1), 105-116. https://doi.org/10.3844/jcssp.2020.105.116

Abstract

Data Mining Techniques are used in many areas like banking, healthcare, education because of extracting relevant information from the database. Many algorithms in data mining are used to predict the disease to reduce the patient treatment cost as well as increase the diagnosis accuracy. In this research work, the risk level of diabetic heart disease patient can be predicted using two datasets such as Pima Indians Diabetes dataset and heart disease dataset. Based on the World Health Organization (WHO), diabetes is the one of the biggest health concerns so mining the diabetes data is an ambiguous task. Diabetic patients may also have to suffer from other diseases like heart disease, eye complications, kidney disease, nerve damage, foot problems, skin complications and dental diseases. However, the existing techniques faced the difficulty to identify inconsistent and redundant features. In this research work, the Random Forest Feature Selection Technique (RFS) is developed to identify the significant features and eliminate irrelevant features which are used to improve the predicting accuracy of Cardio Vascular (CV) disease for diabetic patients. According to the output from the extensive experiments, this research categorizes that whether the diabetic patients having CV or not by using True Positive as diabetes and True Negative as no diabetes.

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Keywords

  • Data Mining
  • Disease Prediction
  • Diabetes
  • Feature Selection
  • Heart Disease
  • Random Forest