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Analysis of Heart Patients Disease Using Data Mining Tool Orange

EasyChair Preprint no. 3672

8 pagesDate: June 24, 2020


In the study of health care is very important now a days in human life. In medical science and their related areas are health concern business has become a notable field in the wide spread area. Health sector are generating lot of information and data which help to understand and need to be analysis, theses data are must convert into meaningful data. To use these patient’s information, make future decision and achieve effective decisions, these decisions help to overcome patients to admit hospital and use expensive treatment. Use patient’s data and implement data mining techniques to learn patients’ patterns and find solution to overcome these diseases. Be that as it may, there is an absence of examining instrument as per furnish compelling test results together with the covered-up data, so and such a framework is created utilizing information digging calculations for characterizing the information and to recognize the heart illnesses. In Healthcare problems data mining provide solution. For heart diseases patient there are 4 algorithms which help to find patterns and solution these are Random Forest, SVM, KNN, Logistic Regression and Naïve Bayes algorithm which help to diagnosis heart issues. In this research paper I am using data mining tool Orange and analyzes parameters and find prediction on heart patients’ diseases and along these lines proposes a heart ailment forecast framework (HDPS) put together aggregate with respect to the data mining approaches.

Keyphrases: Data Mining, Data Mining Classification Techniques, Heart Disease, KNN, Naïve Bayes, ORANGE Tool, Random Forest, SVM

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Danish Umer},
  title = {Analysis of	Heart Patients Disease Using Data Mining Tool Orange},
  howpublished = {EasyChair Preprint no. 3672},

  year = {EasyChair, 2020}}
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