Title

Semi supervised data mining model for the prognosis of pre-diabetic conditions in type 2 Diabetes Mellitus

Authors

A. Sumathi^1*& S. Meganathan²

Affiliation

¹Department of Computer Science Engineering, SASTRA Deemed To be University, Srinivasa Ramanujan Centre (SRC), Kumbakonam, Tamil Nadu, India; ²Department of Computer Science Engineering, SASTRA Deemed To be University, Srinivasa Ramanujan Centre (SRC), Kumbakonam, Tamil Nadu, India

Email

E-mail – sumathi@src.sastra.edu; meganathan@src.sastra.edu; *Corresponding author

Article Type

Research Article

Date

Received December 16, 2019; Revised December 26, 2019; Accepted December 29, 2019; Published December 31, 2019

Diabetic Mellitus is the leading disease in the world irrespective of age and geographical location. It is estimated that 43% of the overall population is affected by the disease. The reasons for the disease include inappropriate diet lifestyle with allied symptoms like obesity. Therefore, the prognosis and diagnosis of the disease are important for adequate combat and care. The prognosis related known symptoms of the disease include incontinence (inability to control urination) and frequent fatigue. Moreover, early prediction of the disease plays an important role in the prognosis of other associated conditions such as heart failure leading to chronic illness. Hence, it is of interest to describe a data mining based prediction model using known features (derived from epidemiological data collected from the public hospital using routine tests) to help in the prognosis of the disease. We used data pre-processing techniques for handling missing values and dimensionality reduction models to improve data quality. The Minimum Description Length principle (MDL) model for discretization (replacing a continuum with a finite set of points) is used to reduce high-level dimensionality of the dataset, which enabled to categorize the dataset into small groups in ordered intervals. Thus, we describe a semi-supervised learning technique (identifies promising attributes using clustering and classification methods) by combining data mining techniques for reasonable accuracy having adequate sensitivity and specificity for further discussion, cross-validation, revaluation, and application. Early prediction of the disease with improved accuracy by analysing specificity ranges in blood pressure and glucose levels will be useful to combat Diabetes Mellitus.

Keywords

Diabetes, semi supervised learning, epidemiological data, prognosis, classification, clustering

Citation

Sumathi & Meganathan, Bioinformation 15(12): 875-881 (2019)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.