Heart Disease Classification for Early Diagnosis based on Adaptive Hoeffding Tree Algorithm in IoMT Data

Ersin Elbasi; Aymen I. Zreikat

doi:10.34028/iajit

Article Details

Heart Disease Classification for Early Diagnosis based on Adaptive Hoeffding Tree Algorithm in IoMT Data

Authors:

Ersin Elbasi | Aymen I. Zreikat

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 1, pp.38-48

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001389564

pISSN:

2309-4524

DOI:

10.34028/iajit

Keywords:

Abstract:

 Heart disease is a rapidly increasing disease that causes death worldwide. Therefore, scientists around the globe start studying this issue from a different perspective to assure early prediction of diagnosis to save patients' life from bad consequences that cause death. In this regard, Internet of Medical Things (IoMT) applications and algorithms should be utilized effectively to overcome this problem. Hoeffding Tree Algorithm (HTA) is a standard decision tree algorithm to handle large sizes of data sets. In this paper, an Adaptive Hoeffding Tree (AHT) algorithm is suggested to carry out classifications of data sets for early diagnosis of heart disease-related factors, and the obtained results by this algorithm are compared with other suggested Machine Learning (ML) algorithms in the literature. Therefore, a total of 3000 records of data sets are used in the classification, 33% of the data are utilized for female patient information, and the rest of the data are utilized for male patient information. In the original data set, each patient record includes 76 attributes, however only the most important 16 patient attributes are used for the classification. Data are retrieved from the University of California Irvine (UCI) Machine Learning Repository, which is collected from the Hungarian Institute of Cardiology, University Hospital at Zurich, University Hospital at Basel, and V.A. Medical Center. The obtained results from this study and the provided comparative results show the effectiveness of the AHT algorithm over other ML algorithms. Compared to other ML algorithms, AHT outperforms other algorithms with 95.67% accuracy for early estimation of diagnosis of heart disease.

Article Details

Heart Disease Classification for Early Diagnosis based on Adaptive Hoeffding Tree Algorithm in IoMT Data

Authors:

Ersin Elbasi | Aymen I. Zreikat

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 1, pp.38-48

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001389564

pISSN:

2309-4524

DOI:

10.34028/iajit

Internet of medical things

machine learning

medical data

random forest

internet of things

diagnosis

AHT

Issues

Article Details

Authors: Ersin Elbasi | Aymen I. Zreikat

Publisher: Zarqa University, Jordan

Journal: The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 1, pp.38-48

Document Type: Article

Publication Date: 1/2023

Document Language: English

Document ID (Org No.): 2001389564

pISSN: 2309-4524

DOI: 10.34028/iajit

Issues

Authors:

Ersin Elbasi | Aymen I. Zreikat

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 1, pp.38-48

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001389564

pISSN:

2309-4524

DOI:

10.34028/iajit