Heart Disease Prediction Using Data Mining Techniques

EasyChair Preprint no. 9772

Abstract

The heart disease prediction - HDP is an important task in health care domain now a day. Because for every minute, the number of people passing away with heart attack. It is difficult to HDP by physicians with huge health records. To overcome this complexity we need to implement the automatic heard disease prediction system to notify the patient and get to recovery from the disease. Here to gaining the automatic system we are using machine learning techniques to easily performing HDP with huge data. The machine learning techniques can be split into multiple types like unsupervised and supervised learning classifier. The unsupervised learning techniques used for prediction with unstructured data. But the supervised learning techniques working with structured data which is recommended to implement this classifiers. So, in this system we are using supervised machine learning techniques such as KNN, RF, NN, DT, NB, and SVM classifiers. For HDP, this system is using training dataset which is accessing from UCI machine learning repository. As well as this system is comparing accuracy performance between various ML - algorithms and shows the accuracy results with graphical presentation.

Keyphrases: Classification, Dataset Collection, prediction

@Booklet{EasyChair:9772,
  author = {Niraj Upadhayaya and Tejaswini Juluri},
  title = {Heart Disease Prediction Using Data Mining Techniques},
  howpublished = {EasyChair Preprint no. 9772},

  year = {EasyChair, 2023}}