skip to main content
10.1145/2905055.2905100acmotherconferencesArticle/Chapter ViewAbstractPublication PagesictcsConference Proceedingsconference-collections
research-article

An implementation of Feature ranking using Machine learning techniques for Diabetes disease prediction

Published: 04 March 2016 Publication History

Abstract

Disease diagnosis is an application area where machine learning tools are providing successful results. Diabetes disease is one of the crucial factors of death all over the world. The availability of huge amounts of medical data leads to the need for powerful learning tools to help medical experts to diagnose diabetes disease. Machine learning methods are helpful in the diagnosis of diabetes disease, showing a reasonable level of efficiency. But these data are redundant and are noisy in nature which negatively affects the process of observing knowledge and useful pattern. Machine learning techniques have attracted a big attention to researchers to turn such data into useful knowledge. Further relevant data can be extracted from huge records using filter based feature selection methods. In our study, a comparative analysis is drawn between four different filter based feature selection methods (Chisquare method, Information gain method, Cluster Variation method and Correlation method) based on Diabetes disease. Three classifiers (RBF, IBK and JRip) were implemented to estimate the performance of the algorithms. The study revealed that filter based feature selection methods enhance the performance of learning algorithms in effective prediction and diagnosis of diabetes disease.

References

[1]
Machine learning algorithms enable discovery of important regularities in large datasets November 1999/Vol. 42, No. 11 COMMUNICATIONS OF THE ACM
[2]
J. Novakovic, P. Strbac, and D. Bulatovic, "Toward optimal feature selection using ranking methods and classification algorithms," Yugoslav Journal of Operations Research, vol. 21, no. 1, pp. 119--135, 2011.
[3]
Sarvestan Soltani A., Safavi A. A., Parandeh M. N. and Salehi M., "Predicting Breast Cancer Survivability using data mining techniques", Software Technology and Engineering (ICSTE), 2nd International Conference, pp. 227--231, Vol. 2, 2010.
[4]
C. M. Velu and K. R. Kashwan, "Visual Data Mining Techniques for Classification of Diabetic Patients", 3rd IEEE International Advance Computing Conference (IACC), 2013.
[5]
Sankaranarayanan.S and Dr Pramananda Perumal. T, "Predictive Approach for Diabetes Mellitus Disease through Data Mining Technologies", World Congress on Computing and Communication Technologies, 2014, pp. 231--233.
[6]
Mostafa Fathi Ganji and Mohammad Saniee Abadeh, "Using fuzzy Ant Colony Optimization for Diagnosis of Diabetes Disease", Proceedings of ICEE 2010, May 11-13, 2010.
[7]
Mostafa Fathi Ganji and Mohammad Saniee Abadeh, "Using fuzzy Ant Colony Optimization for Diagnosis of Diabetes Disease", Proceedings of ICEE 2010, May 11-13, 2010.
[8]
Sonu Kumari and Archana Singh, "A Data Mining Approach for the Diagnosis of Diabetes Mellitus", Proceedings of 71h international Conference on Intelligent Systems and Control (ISCO 2013).
[9]
M. Ashraf, G. Chetty, and D. Tran, "Feature selection techniques on thyroid, hepatitis, and breast cancer datasets," International Journal on Data Mining and Intelligent Information Technology Applications(IJMIA),vol. 3, no. 1, pp. 1--8, 2013.
[10]
M. Leach, "Parallelising feature selection algorithms," University of Manchester, Manchester, 2012.
[11]
I. T. Jolliffe. (2002). Principal Component Analysis. {Online}. Available: http://books.google.com.au
[12]
WEKA: Weka 3: Data Mining Software in Java. {Online}. Available: http://www.cs.waikato.ac.nz/ml/weka

Cited By

View all
  • (2024)An Explainable Prediction for Dietary-Related Diseases via Language ModelsNutrients10.3390/nu1605068616:5(686)Online publication date: 28-Feb-2024
  • (2024)Identify the Factors Influencing Suicide among Ardabil city People Using Feature Selection : Identify the Factors Influencing Suicide among Ardabil using machine learning2024 10th International Conference on Artificial Intelligence and Robotics (QICAR)10.1109/QICAR61538.2024.10496603(17-23)Online publication date: 29-Feb-2024
  • (2024)Contactor Fault Detection and Classification System Using Optical Fiber Bragg Grating SensorsIEEE Sensors Journal10.1109/JSEN.2023.334718924:4(5316-5323)Online publication date: 15-Feb-2024
  • Show More Cited By
  1. An implementation of Feature ranking using Machine learning techniques for Diabetes disease prediction

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies
    March 2016
    843 pages
    ISBN:9781450339629
    DOI:10.1145/2905055
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 04 March 2016

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. F-Score
    2. Feature Ranking
    3. Feature Selection
    4. Filters
    5. IBK
    6. JRip
    7. RBF
    8. RMSE

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ICTCS '16

    Acceptance Rates

    Overall Acceptance Rate 97 of 270 submissions, 36%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)18
    • Downloads (Last 6 weeks)2
    Reflects downloads up to 20 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)An Explainable Prediction for Dietary-Related Diseases via Language ModelsNutrients10.3390/nu1605068616:5(686)Online publication date: 28-Feb-2024
    • (2024)Identify the Factors Influencing Suicide among Ardabil city People Using Feature Selection : Identify the Factors Influencing Suicide among Ardabil using machine learning2024 10th International Conference on Artificial Intelligence and Robotics (QICAR)10.1109/QICAR61538.2024.10496603(17-23)Online publication date: 29-Feb-2024
    • (2024)Contactor Fault Detection and Classification System Using Optical Fiber Bragg Grating SensorsIEEE Sensors Journal10.1109/JSEN.2023.334718924:4(5316-5323)Online publication date: 15-Feb-2024
    • (2024)Diabetic Retinopathy Identification Using Optimized Deep CNN Classifier2024 International Conference on Emerging Systems and Intelligent Computing (ESIC)10.1109/ESIC60604.2024.10481653(199-206)Online publication date: 9-Feb-2024
    • (2024)Criminal Psychological Profiling Using a Hybrid Bayesian Network ModelProceedings of Data Analytics and Management10.1007/978-981-99-6547-2_42(553-561)Online publication date: 3-Jan-2024
    • (2023)The Effect of Feature Selection on Diabetes Prediction Using Machine Learning2023 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC58397.2023.10218243(1-7)Online publication date: 9-Jul-2023
    • (2022)Automatic Lung Carcinoma Identification and Classification in CT Images Using CNN Deep Learning ModelAugmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis10.1007/978-981-19-1076-0_9(143-166)Online publication date: 20-Apr-2022
    • (2022)Cognitive Computing Driven Healthcare: A Precise StudyAugmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis10.1007/978-981-19-1076-0_14(259-279)Online publication date: 20-Apr-2022
    • (2022)NLP Applications for Big Data Analytics Within HealthcareAugmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis10.1007/978-981-19-1076-0_13(237-257)Online publication date: 20-Apr-2022
    • (2022)Song Recommendation Using Mood Detection with Xception ModelCognitive Informatics and Soft Computing10.1007/978-981-16-8763-1_40(491-501)Online publication date: 31-May-2022
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media