Open AccessMachine Learning Based Diagnosis and Prediction System for Congestive Heart Failure
Author(s) -
Niharika Saxena,
Dr L.S. Maurya
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.j9190.0881019
Subject(s) - decision tree , machine learning , death toll , computer science , heart failure , artificial intelligence , work (physics) , disease , health care , predictive modelling , heart disease , medicine , engineering , mechanical engineering , environmental health , economics , economic growth
Recently, heart failure has become one of the major Causes of death. By 2030, if it is not controlled the toll will rise to twenty three million. Cardiologist can predict the disease with 70 % accuracy. Considering the limitation of cardiologist, a system can be provided to them to predict the disease with more accuracy. Machine Learning is frequently used in to days world to support healthcare industry. ML provides new opportunity to analyze the data with more accuracy. It bridges the gap between medical science and technology. Decision tree is one of the best classification techniques of machine learning which will analyze the data and predict the disease with accuracy. The main objective of my dissertation work is to predict the disease and analyze the result. So in this research work the DT technique is used for the prediction of disease and it gave result with more accuracy on comparison to previous work. Hence this study proved that DT algorithm gives the result with more accuracy in less time of execution. This research work is a growing range of efficient tools to assist healthcare industry and medical professionals for the betterment of patients.