Earticle

초록

영어

Accuracy improvement of classification model becomes main research objective in various fields. Selecting important features and removing outliers of a dataset are two effective solutions for improving model accuracy. Information Gain is one of the feature selection methods that can be considered as a solution for selecting important features of a dataset. Information Gain selects the variable that maximizes the information gain, which in turn minimizes the entropy and best splits the dataset into groups for effective classification. Aside of selecting important feature, removing outlier is also necessary for improving accuracy of the classification model. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the powerful outlier removal methods which can identify with significant accuracy the clusters of random shape and size in large databases corrupted with noise. Therefore, in this study, we propose the accuracy improvement of heart disease classification model using Information Gain and DBSCAN applied to various machine learning algorithms. One publicly available heart disease dataset (Cleveland) is utilized in this study to build the classification model. The results showed that after implementing Information Gain, the accuracy of the model applied to Gaussian Naïve Bayes, Logistic Regression, Multi-Layer Perceptron, Support Vector Machine, Decision Tree, Random Forest, and Extreme Gradient Boosting algorithms increases as much as 1.31% in average. The accuracy also increases when DBSCAN is applied to the model after utilizing Information Gain, with the number of improvements is around 0.62%.

목차

Abstract
Introduction
Methods
Dataset
Information Gain
DBSCAN
Machine Learning Model
Result and Discussion
Conclusion
Acknowledgments
References

키워드

저자

• Norma Latif Fitriyani [ Department of Data Science, Sejong University ]
• Muhammad Syafrudin [ Department of Artificial Intelligence, Sejong University ]
• Ganjar Alfian [ Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658