Eye Refractive Error Classification Using Machine Learning Techniques

被引：0

作者：

Fageeri, Sallam Osman ^{[1
]}

Ahmed, Shyma Mogtaba Mohammed ^{[2
]}

Almubarak, Sahar Abdalla ^{[3
]}

Mu'azu, Abubakar Aminu ^{[4
]}

机构：

[1] Alzaiem Alazhari Univ, Fac Comp Sci & Informat Technol, Khartoum, Sudan

[2] Univ Khartoum, Fac Math Sci, Khartoum, Sudan

[3] Univ Sci & Technol, Fac Informat Technol, Khartoum, Sudan

[4] Umaru Musa Yaradua Univ Katsina, Dept Math & Comp Sci, Katsina, Nigeria

来源：

2017 INTERNATIONAL CONFERENCE ON COMMUNICATION, CONTROL, COMPUTING AND ELECTRONICS ENGINEERING (ICCCCEE) | 2017年

关键词：

Machine learning; Data Mining; Classification;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Machine learning is a subdivision of Artificial Intelligence (AI) that is concerned with the design and development of intelligent algorithms that enables machines to learn from data without being programmed. Machine learning mainly focus on how to automatically recognize complex patterns among data and make intelligent decisions. In this paper, intelligent machine learning algorithms are used to classify the type of an eye disease based on ophthalmology data collected from patients of Mecca hospital in Sudan. Three machine-learning techniques are used to predict the severity of the eye that occurred during the investigation, which are Naive Bayesian, SVM, and J48 decision tree. The obtained result showed that J48 classifier outperforms both Naive Bayesian as well as SVM.

引用

页数：6

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Quellec, Gwenole ;

Lamard, Mathieu ;

Erginay, Ali ;

Chabouis, Agnes ;

Massin, Pascale ;

Cochener, Beatrice ;

Cazuguel, Guy .

MEDICAL IMAGE ANALYSIS, 2016, 29 :47-64

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