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Medical Data Classification using Machine Learning Techniques

by Koby Bond, Alaa Sheta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 6
Year of Publication: 2021
Authors: Koby Bond, Alaa Sheta
10.5120/ijca2021921339

Koby Bond, Alaa Sheta . Medical Data Classification using Machine Learning Techniques. International Journal of Computer Applications. 183, 6 ( Jun 2021), 1-8. DOI=10.5120/ijca2021921339

@article{ 10.5120/ijca2021921339,
author = { Koby Bond, Alaa Sheta },
title = { Medical Data Classification using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2021 },
volume = { 183 },
number = { 6 },
month = { Jun },
year = { 2021 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number6/31928-2021921339/ },
doi = { 10.5120/ijca2021921339 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:59.837204+05:30
%A Koby Bond
%A Alaa Sheta
%T Medical Data Classification using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 6
%P 1-8
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Medical data classification is a challenging problem in the data mining field. It can be defined as the process of splitting (i.e., categorizing) data into appropriate groups (i.e., classes) based on their common characteristics. The classification of medical data is a significant data mining problem explored in various real-world applications by numerous researchers. In this research, we provide a detailed comparison between several machine learning classification approaches and explored their predictive accuracy on several datasets. They include Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Decision Trees (DT). The quality of the developed classifiers was evaluated using several criteria such as Precision, Recall, and F-Measure. Several data set from the UCI Machine Learning Repository (i.e., Pima Indians Diabetes and the Breast Cancer Coimbra datasets) was used for this study. The experimental results reveal that the ANN-based classifier was the most accurate classification in all cases, with its ROC area being the highest.

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Index Terms

Computer Science
Information Sciences

Keywords

Medical Data Classification Machine Learning Neural Networks Support Vector Machines Decision Trees

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