Advertisement

Clinical Decision Support System for Neuro-Degenerative Disorders: An Optimal Feature Selective Classifier and Identification of Predictor Markers

  • Lokeswari VenkataramanaEmail author
  • Shomona Gracia Jacob
  • S. Saraswathi
  • R. Athilakshmi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

Detecting divergence between Neuro-degenerative diseases is essential for right treatment. This intelligent system is implemented through computational methods to predict the class of Neuro-degenerative disease (Alzheimer’s, Parkinson’s or common) from the structural and physicochemical properties (1437 attributes respectively) of protein sequences extracted from genes. The Gene Set Enrichment Analysis database (GSEA db) was utilized to obtain the gene sets that contributed to the development of Alzheimer’s and Parkinson’s disease. Optimal features for classification were obtained by applying Gain Ratio followed by Correlation-based Feature Selection (CFS) and Decremental Feature Selection (DFS) on extracted properties from Kyoto Encyclopedia of Genes and Genomes (KEGG) dataset for the GSEA database. The selected features are evaluated using Random Forest model. The Clinical Decision Support System (CDSS) was build which extract rules from the least sized Decision tree automatically and predict the type of Neuro-degenerative disorder as Alzheimer’s disease, Parkinson’s disease or common to both diseases. The CDSS predicts the disease with classification accuracy as 79.7% and Mathew’s Correlation Coefficient as 0.689.

Keywords

Neuro-degenerative disorder Alzheimer’s disease Parkinson’s disease Prediction system Clinical Decision Support System 

Notes

Acknowledgements

This research work is part of project work funded by Science and Engineering Research Board (SERB), Department of Science and Technology (DST) funded project under Young Scientist Scheme – Early Start-up Research Grant- titled “Investigation on the effect of Gene and Protein Mutants in the onset of Neuro-Degenerative Brain Disorders (Alzheimer’s and Parkinson’s disease): A Computational Study” with Reference No- SERB – YSS/2015/000737.

References

  1. 1.
    Brain Disorders by Numbers (for brain research at MIT) (2018). https://mcgovern.mit.edu/brain-disorders/by-the-numbers. Accessed 03 June 2018
  2. 2.
    Jacob, S.G., Athilakshmi, R.: Extraction of protein sequence features for prediction of neuro-degenerative brain disorders: Pioneering the CGAP database. In: Proceedings of the International Conference on Informatics and Analytics, p. 30. ACM, August 2016Google Scholar
  3. 3.
    Shree, S.B., Sheshadri, H.S.: Diagnosis of Alzheimer’s disease using rule based approach. Indian J. Sci. Technol. 9(13) (2016)Google Scholar
  4. 4.
    Shree, S.B., Sheshadri, H.S.: An initial investigation in the diagnosis of Alzheimer’s disease using various classification techniques. In: 2014 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–5. IEEE, December 2014Google Scholar
  5. 5.
    Bind, S., Tiwari, A.K., Sahani, A.K., Koulibaly, P.M., Nobili, F., Pagani, M., Sabri, O., Borght, T.V., Laere, K.V., Tatsch, K.: A survey of machine learning based approaches for Parkinson disease prediction. Int. J. Comput. Sci. Inform. Technol. 6(2), 1648–1655 (2015)Google Scholar
  6. 6.
    Ramani, R.G., Sivagami, G.: Parkinson disease classification using data mining algorithms. Int. J. Comput. Appl. 32(9), 17–22 (2011)Google Scholar
  7. 7.
    Ramani, R.G., Jacob, S.G.: Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models. PLoS ONE 8(3), e58772 (2013)CrossRefGoogle Scholar
  8. 8.
    Kaladhar, D.S.V.G.K., Pottumuthu, B.K., Rao, P.V.N., Vadlamudi, V., Chaitanya, A.K., Reddy, R.H.: The elements of statistical learning in colon cancer datasets: data mining, inference and prediction. Algorithms Res. 2(1), 8–17 (1926)Google Scholar
  9. 9.
    Hoogendoorn, M., Moons, L.M., Numans, M.E., Sips, R.J.: Utilizing data mining for predictive modeling of colorectal cancer using electronic medical records. In: International Conference on Brain Informatics and Health, pp. 132–141. Springer, Cham (2014)Google Scholar
  10. 10.
    KEGG dataset for neuro-degenerative diseases (2018). http://www.genome.jp/kegg-bin/gethtext?br08402.keg. Accessed 6 June 2018
  11. 11.
    Uniprot database Server, 22 June 2017. http://www.uniprot.org. Accessed 6 June 2018
  12. 12.
    PROFEAT web server (2018). http://bidd2.nus.edu.sg/cgi-bin/prof2015/prof_home.cgi. Accessed 05 June 2018
  13. 13.
    Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, New York (2011)zbMATHGoogle Scholar
  14. 14.
    Karegowda, A.G., Manjunath, A.S., Jayaram, M.A.: Comparative study of attribute selection using gain ratio and correlation based feature selection. Int. J. Inf. Technol. Knowl. Manag. 2(2), 271–277 (2010)Google Scholar
  15. 15.
    Jacob, S.G., Ramani, R.G., Nancy, P.: Feature selection and classification in breast cancer datasets through data mining algorithms. In Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC 2011), Kanyakumari, India, IEEE Catalog Number: CFP1120 J-PRT, December 2011, ISBN 978-1Google Scholar
  16. 16.
    Hall, M.A.: Correlation-based feature selection for machine learning (1999)Google Scholar
  17. 17.
    Doshi, M.: Correlation based feature selection (Cfs) technique to predict student perfromance. Int. J. Comput. Networks Commun. 6(3), 197 (2014)CrossRefGoogle Scholar
  18. 18.
    Richards, J.W., Eads, D., Bloom, J.S., Brink, H., Starr, D.: WiseRFTM: a fast and scalable Random Forest (2013)Google Scholar
  19. 19.
    Tejeswinee, K., Jacob, S.G.: Binary classification of cognitive disorders: investigation on the effects of protein sequence properties in Alzheimer’s and Parkinson’s disease. In: IAENG-IMECS 2017, 166–170 (2017)Google Scholar
  20. 20.
    Implementing WEKA in java, 03 June 2018. https://weka.wikispaces.com/Use+WEKA+in+your+Java+code

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Lokeswari Venkataramana
    • 1
    Email author
  • Shomona Gracia Jacob
    • 1
  • S. Saraswathi
    • 1
  • R. Athilakshmi
    • 1
  1. 1.Sri Sivasubramaniya Nadar College of EngineeringChennaiIndia

Personalised recommendations