A comparison of data mining methods for diagnosis and prognosis of heart disease Online publication date: Mon, 20-Apr-2020
by Mohammad Reza Afrash; Mehdi Khalili; Maral Sedigh Salekde
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 16, No. 1, 2020
Abstract: Heart disease is a term that covers a range of disorders that affect heart. Medical diagnosis is a very important and it is a complicated task that should be performed properly and efficiently. The needs for new tools able to help doctors in predicting and diagnosis heart disease which is highly recognised. In the present study first, the dataset containing total instances of 439 with 14 attributes were obtained from several medical centres in Tehran, Iran. Secondly, five data mining techniques were used: the naïve Bayes, ANN, random forest and C4.5 decision trees and random tree algorithms. From the experimental result it is observed that the Random forest algorithm with (94.53, 0.945, 0.969, 0.945, 0.916) for accuracy, F-measure, specificity, sensitivity and kappa rate produce a higher performance for our classification model when compared with other algorithms.
Online publication date: Mon, 20-Apr-2020
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com