The Clustered Similarity (CS) Approach for Relevant Disease Information Extraction

N.Satyanandam*, Satyanarayana**
* Associate Professor, Department of Computer Science and Engineering, Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India.
** Professor, Department of Computer Science and Engineering, JNTUK, University College of Engineering, Kakinada, Andhra Pradesh, India.
Periodicity:October - December'2015


The Machine Learning field has picked up its push in any space of examination and just as of late has turned into a dependable apparatus in the therapeutic area. The experiential area of programmed learning is utilized as a part of assignments, for example, restorative choice bolster, medicinal imaging, protein-protein collaboration, extraction of therapeutic information, and for general patient administration care. Machine Learning (ML) is imagined as a device by which PC based frameworks can be incorporated in the medicinal services field to show signs of improvement, all around sorted out therapeutic consideration. It depicts a ML-based strategy for building an application that is fit for distinguishing and dispersing social insurance data. It concentrates sentences from distributed medicinal papers that say maladies and medications, and distinguishes semantic relations that exist in the middle of ailments and medicines. This paper proposes a new way of information retrieval. A clustered similarity approach is used to overcome the previous approach’s drawbacks. Results are obtained and the proposed work is much better than existing techniques.


Similarity, Clustering, Bag-of-words, Classification.

How to Cite this Article?

Satyanandam, N., and Satyanarayana (2015). The Clustered Similarity (CS) Approach for Relevant Disease Information Extraction. i-manager’s Journal on Software Engineering, 10(2), 1-5.


[1]. Pedrosa, G., Rahman, M., Antani, S., Demner- Fushman, D., Long, L., and Traina, A. (2014). “Integrating visual words as bunch of n-grams for effective biomedical image classification”, IEEE Winter Conference on Applications of Computer Vision.
[2]. Julina, J., and Thenmozhi, D. (2012). “Ontology based EMR for decision making in health care using SNOMED CT”, 2012 International Conference on Recent Trends in Information Technology.
[3]. Champion, H., Pizzi, N., and Krishnamoorthy, R. (2015). “Tactical Clinical Text Mining for Improved Patient Characterization”, 2014 IEEE International Congress on Big Data.
[4]. Lakshmi, K., and Kumar, G. (2014). “Association rule extraction from medical transcripts of diabetic patients”, The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014).
[5]. Dasgupta, A., Drineas, P., Harb, B., Josifovski, V., and Mahoney, M. (2013). “Feature selection methods for text classification”, Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '07.
[6]. Raghavan, P. (2004). “Text Centric Structure Extraction and Exploitation”, Proceedings of the 7th International Workshop on the Web and Databases Colocated with ACM SIGMOD/PODS 2004 - WebDB '04.
[7]. Oracle corporation, (2008). Retrieved from
[8]. Merrill lynch, (2000). “e-Business Analytics: Depth Report”.
[9]. Pegah Falinouss, “Stock Trend Prediction using News Article's: a text mining approach”, Master thesis.
[10]. Sebastiani, F. (2002). “Machine learning in automated text categorization”, CSUR ACM Comput. Surv. ACM Computing Surveys, pp.1-47.
[12]. Dinu, L.P, and Ionescu, R.T (2012). “A Rank-Based Approach of Cosine Similarity with Applications in Automatic Classification”, Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2012 14th International Symposium, IEEE.

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.