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Heart Patients Datasets Analysis using Weka Tool

EasyChair Preprint no. 3656

8 pagesDate: June 21, 2020


In health department human task are increase and different techniques are available for doctors to cure patients. In Medical business is a worldwide business and people want research on latest patient’s data. Health field become an outstanding field in the wide spread territory of restorative science and they’re very popular field now a days. Huge amount of raw data in medical side are need to be convert use full data this data has hidden information and patterns to which we can learn and make accurate decisions in future. These decisions are applying to patients and cure the patients, with the help of data mining they reduce patients’ tests, money and time. Lack of analysis of data mining tool as indicated by successful test results together with the covered-up data, so and such a framework is created utilizing information-digging calculations for arranging the data patterns with help to more, identify heart patients’ issues. The data need to be classified and make visualization using best available tool. Using data mining tools, they provide better solutions. in my case study i use 5 major classification algorithms to find accuracy in heart patient’s diseases, these algorithms are: k-Nearest Neighbors (KNN), Linear Regression (LR), Random Forest (RM), Naive Bayes (NB), Support Vector Machine (SVM). These data mining methods can provide solution for heart patients. In this paper they analysis some few prediction and parameters for heart patients also suggests HDPS (Heart Diseases Prediction System) put together aggregate with respect to the data mining approaches.

Keyphrases: Data Mining, Heart Disease, k-nearest neighbors, linear regression, Naïve Bayes, Random Forest, Support Vector Machine, WEKA tool

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Fazeela Maryum},
  title = {Heart Patients Datasets Analysis using Weka Tool},
  howpublished = {EasyChair Preprint no. 3656},

  year = {EasyChair, 2020}}
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