A computational approach of data smoothening and prediction of diabetes dataset

Shivani Jakhmola, Tribikram Pradhan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Data mining when applied on medical diagnosis can help doctors to take major decisions. Diabetes is a disease which has to be monitored by the patient so as not to cause severe damage to the body. Therefore to predict diabetes is an important task that is most important for the patient. In this study, a new data smoothening technique is proposed for noise removal from the data. It is very important for the user to have control over the smoothening of the data so that the information loss can be monitored. The proposed method allows the user to control the level of data smoothening by accepting the loss percentage on the individual data points. Allowable loss is calculated and a decision is made to smoothen the value or retain it to the level which is accurate. The proposed method will enable the user to get the output based on his requirements of preprocessing. The proposed algorithm will allow the user to interact with the data preprocessing system unlike the primitive algorithms. Different levels of smoothened output are obtained by different loss percentage. This preprocessed output produced will be of a better quality and will resemble more to the real world data. Furthermore, correlation and multiple regression is applied on the preprocessed diabetes dataset and a prediction is made on this basis.

Original languageEnglish
Title of host publicationProceeding of the 3rd International Symposium on Women in Computing and Informatics, WCI 2015
PublisherAssociation for Computing Machinery (ACM)
Pages744-748
Number of pages5
Volume10-13-August-2015
ISBN (Electronic)9781450333610
DOIs
Publication statusPublished - 10-08-2015
Event3rd International Symposium on Women in Computing and Informatics, WCI 2015 - Kerala, India
Duration: 10-08-201513-08-2015

Conference

Conference3rd International Symposium on Women in Computing and Informatics, WCI 2015
CountryIndia
CityKerala
Period10-08-1513-08-15

Fingerprint

Medical problems
Data mining

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Jakhmola, S., & Pradhan, T. (2015). A computational approach of data smoothening and prediction of diabetes dataset. In Proceeding of the 3rd International Symposium on Women in Computing and Informatics, WCI 2015 (Vol. 10-13-August-2015, pp. 744-748). Association for Computing Machinery (ACM). https://doi.org/10.1145/2791405.2791572
Jakhmola, Shivani ; Pradhan, Tribikram. / A computational approach of data smoothening and prediction of diabetes dataset. Proceeding of the 3rd International Symposium on Women in Computing and Informatics, WCI 2015. Vol. 10-13-August-2015 Association for Computing Machinery (ACM), 2015. pp. 744-748
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Jakhmola, S & Pradhan, T 2015, A computational approach of data smoothening and prediction of diabetes dataset. in Proceeding of the 3rd International Symposium on Women in Computing and Informatics, WCI 2015. vol. 10-13-August-2015, Association for Computing Machinery (ACM), pp. 744-748, 3rd International Symposium on Women in Computing and Informatics, WCI 2015, Kerala, India, 10-08-15. https://doi.org/10.1145/2791405.2791572

A computational approach of data smoothening and prediction of diabetes dataset. / Jakhmola, Shivani; Pradhan, Tribikram.

Proceeding of the 3rd International Symposium on Women in Computing and Informatics, WCI 2015. Vol. 10-13-August-2015 Association for Computing Machinery (ACM), 2015. p. 744-748.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Jakhmola S, Pradhan T. A computational approach of data smoothening and prediction of diabetes dataset. In Proceeding of the 3rd International Symposium on Women in Computing and Informatics, WCI 2015. Vol. 10-13-August-2015. Association for Computing Machinery (ACM). 2015. p. 744-748 https://doi.org/10.1145/2791405.2791572