Prediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers

V. Sai Krishna Reddy, P. Meghana, N. V.Subba Reddy, B. Ashwath Rao

Research output: Contribution to journalConference articlepeer-review

11 Citations (Scopus)

Abstract

Machine Learning is an application of Artificial Intelligence where the method begins with observations on data. In the medical field, it is very important to make a correct decision within less time while treating a patient. Here ML techniques play a major role in predicting the disease by considering the vast amount of data that is produced by the healthcare field. In India, heart disease is the major cause of death. According to WHO, it can predict and prevent stroke by timely actions. In this paper, the study is useful to predict cardiovascular disease with better accuracy by applying ML techniques like Decision Tree and Naïve Bayes and also with the help of risk factors. The dataset that we considered is the Heart Failure Dataset which consists of 13 attributes. In the process of analyzing the performance of techniques, the collected data should be pre-processed. Later, it should follow by feature selection and reduction.

Original languageEnglish
Article number012015
JournalJournal of Physics: Conference Series
Volume2161
Issue number1
DOIs
Publication statusPublished - 11-01-2022
Event1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021 - Manipal, Virtual, India
Duration: 28-10-202130-10-2021

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy

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