Open AccessPrediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers
Author(s) -
V Sai Krishna Reddy,
Pasam Meghana,
Neelima G. Reddy,
B. Ashwath Rao
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2161/1/012015
Subject(s) - decision tree , naive bayes classifier , feature selection , computer science , artificial intelligence , machine learning , field (mathematics) , disease , bayes' theorem , feature (linguistics) , process (computing) , data mining , medicine , bayesian probability , support vector machine , mathematics , linguistics , philosophy , pure mathematics , operating system
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.