Hybrid Approach for Diagnosing Thyroid, Hepatitis, and Breast Cancer Based on Correlation Based Feature Selection and Naïve Bayes

Mohammad Ashraf Ali Bani Ahmad, Girija Chetty, Dat Tran, Dharmendra Sharma

Research output: A Conference proceeding or a Chapter in BookConference contribution

4 Citations (Scopus)

Abstract

Feature selection techniques have become an obvious need for researchers in computer science and many other fields of science. Whether the target research is in medicine, agriculture, business, or industry; the necessity for analysing large amount of data is needed. In Addition to that, finding the most excellent feature selection technique that best satisfies a certain learning algorithm could bring the benefit for researchers. Therefore, we proposed a new method for diagnosing some diseases based on a combination of learning algorithm tools and feature selection techniques. The idea is to obtain a hybrid approach that combines the best performing learning algorithms and the best performing feature selection techniques in regards to three well-known datasets. Experimental result shows that co-ordination between correlation based feature selection method along with Naive Bayse learning algorithm can produce promising results.
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2012)
Subtitle of host publicationLecture Notes in Computer Science
EditorsTingwen Huang, Zhigang Zeng, Chuangdong Li, Chi Sing Leung
Place of PublicationBerlin, Germany
PublisherSpringer
Pages272-280
Number of pages9
Volume7666
ISBN (Print)9783642344787
DOIs
Publication statusPublished - 2012
Event19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Conference

Conference19th International Conference on Neural Information Processing 2012
CountryQatar
CityDoha
Period12/11/1215/11/12

Fingerprint

Feature extraction
Learning algorithms
Agriculture
Computer science
Medicine
Industry

Cite this

Bani Ahmad, M. A. A., Chetty, G., Tran, D., & Sharma, D. (2012). Hybrid Approach for Diagnosing Thyroid, Hepatitis, and Breast Cancer Based on Correlation Based Feature Selection and Naïve Bayes. In T. Huang, Z. Zeng, C. Li, & C. S. Leung (Eds.), International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science (Vol. 7666, pp. 272-280). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-34478-7_34
Bani Ahmad, Mohammad Ashraf Ali ; Chetty, Girija ; Tran, Dat ; Sharma, Dharmendra. / Hybrid Approach for Diagnosing Thyroid, Hepatitis, and Breast Cancer Based on Correlation Based Feature Selection and Naïve Bayes. International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. editor / Tingwen Huang ; Zhigang Zeng ; Chuangdong Li ; Chi Sing Leung. Vol. 7666 Berlin, Germany : Springer, 2012. pp. 272-280
@inproceedings{7f684fd16a9149e8aeb3e92ab56b6bcd,
title = "Hybrid Approach for Diagnosing Thyroid, Hepatitis, and Breast Cancer Based on Correlation Based Feature Selection and Na{\~A}¯ve Bayes",
abstract = "Feature selection techniques have become an obvious need for researchers in computer science and many other fields of science. Whether the target research is in medicine, agriculture, business, or industry; the necessity for analysing large amount of data is needed. In Addition to that, finding the most excellent feature selection technique that best satisfies a certain learning algorithm could bring the benefit for researchers. Therefore, we proposed a new method for diagnosing some diseases based on a combination of learning algorithm tools and feature selection techniques. The idea is to obtain a hybrid approach that combines the best performing learning algorithms and the best performing feature selection techniques in regards to three well-known datasets. Experimental result shows that co-ordination between correlation based feature selection method along with Naive Bayse learning algorithm can produce promising results.",
keywords = "Feature selection methods, Learning algorithms, Hybrid systems, Data mining, Breast cancer dataset, Thyroid, Hepatitis",
author = "{Bani Ahmad}, {Mohammad Ashraf Ali} and Girija Chetty and Dat Tran and Dharmendra Sharma",
year = "2012",
doi = "10.1007/978-3-642-34478-7_34",
language = "English",
isbn = "9783642344787",
volume = "7666",
pages = "272--280",
editor = "Tingwen Huang and Zhigang Zeng and Chuangdong Li and Leung, {Chi Sing}",
booktitle = "International Conference on Neural Information Processing (ICONIP 2012)",
publisher = "Springer",
address = "Netherlands",

}

Bani Ahmad, MAA, Chetty, G, Tran, D & Sharma, D 2012, Hybrid Approach for Diagnosing Thyroid, Hepatitis, and Breast Cancer Based on Correlation Based Feature Selection and Naïve Bayes. in T Huang, Z Zeng, C Li & CS Leung (eds), International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. vol. 7666, Springer, Berlin, Germany, pp. 272-280, 19th International Conference on Neural Information Processing 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34478-7_34

Hybrid Approach for Diagnosing Thyroid, Hepatitis, and Breast Cancer Based on Correlation Based Feature Selection and Naïve Bayes. / Bani Ahmad, Mohammad Ashraf Ali; Chetty, Girija; Tran, Dat; Sharma, Dharmendra.

International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. ed. / Tingwen Huang; Zhigang Zeng; Chuangdong Li; Chi Sing Leung. Vol. 7666 Berlin, Germany : Springer, 2012. p. 272-280.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Hybrid Approach for Diagnosing Thyroid, Hepatitis, and Breast Cancer Based on Correlation Based Feature Selection and Naïve Bayes

AU - Bani Ahmad, Mohammad Ashraf Ali

AU - Chetty, Girija

AU - Tran, Dat

AU - Sharma, Dharmendra

PY - 2012

Y1 - 2012

N2 - Feature selection techniques have become an obvious need for researchers in computer science and many other fields of science. Whether the target research is in medicine, agriculture, business, or industry; the necessity for analysing large amount of data is needed. In Addition to that, finding the most excellent feature selection technique that best satisfies a certain learning algorithm could bring the benefit for researchers. Therefore, we proposed a new method for diagnosing some diseases based on a combination of learning algorithm tools and feature selection techniques. The idea is to obtain a hybrid approach that combines the best performing learning algorithms and the best performing feature selection techniques in regards to three well-known datasets. Experimental result shows that co-ordination between correlation based feature selection method along with Naive Bayse learning algorithm can produce promising results.

AB - Feature selection techniques have become an obvious need for researchers in computer science and many other fields of science. Whether the target research is in medicine, agriculture, business, or industry; the necessity for analysing large amount of data is needed. In Addition to that, finding the most excellent feature selection technique that best satisfies a certain learning algorithm could bring the benefit for researchers. Therefore, we proposed a new method for diagnosing some diseases based on a combination of learning algorithm tools and feature selection techniques. The idea is to obtain a hybrid approach that combines the best performing learning algorithms and the best performing feature selection techniques in regards to three well-known datasets. Experimental result shows that co-ordination between correlation based feature selection method along with Naive Bayse learning algorithm can produce promising results.

KW - Feature selection methods

KW - Learning algorithms

KW - Hybrid systems

KW - Data mining

KW - Breast cancer dataset

KW - Thyroid

KW - Hepatitis

U2 - 10.1007/978-3-642-34478-7_34

DO - 10.1007/978-3-642-34478-7_34

M3 - Conference contribution

SN - 9783642344787

VL - 7666

SP - 272

EP - 280

BT - International Conference on Neural Information Processing (ICONIP 2012)

A2 - Huang, Tingwen

A2 - Zeng, Zhigang

A2 - Li, Chuangdong

A2 - Leung, Chi Sing

PB - Springer

CY - Berlin, Germany

ER -

Bani Ahmad MAA, Chetty G, Tran D, Sharma D. Hybrid Approach for Diagnosing Thyroid, Hepatitis, and Breast Cancer Based on Correlation Based Feature Selection and Naïve Bayes. In Huang T, Zeng Z, Li C, Leung CS, editors, International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. Vol. 7666. Berlin, Germany: Springer. 2012. p. 272-280 https://doi.org/10.1007/978-3-642-34478-7_34