Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning

Rohit Bharti; Aditya Khamparia; Mohammad Shabaz; Gaurav Dhiman; Sagar Pande; Parneet Singh

doi:10.1155/2021/8387680

Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning

Comput Intell Neurosci. 2021 Jul 1:2021:8387680. doi: 10.1155/2021/8387680. eCollection 2021.

Authors

Rohit Bharti¹, Aditya Khamparia², Mohammad Shabaz³, Gaurav Dhiman⁴, Sagar Pande¹, Parneet Singh⁵

Affiliations

¹ School of Computer Science and Engineering, Lovely Professional University, Phagwara, India.
² Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India.
³ Arba Minch University, Arba Minch, Ethiopia.
⁴ Department of Computer Science, Government Bikram College of Commerce, Patiala, India.
⁵ All India Institute of Medical Science, Rishikesh, India.

Abstract

The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.

MeSH terms

Algorithms
Deep Learning*
Heart Diseases* / diagnosis
Humans
Machine Learning