A computational approach of data smoothening and prediction of diabetes dataset

Shivani Jakhmola; Tribikram Pradhan

doi:10.1145/2791405.2791572

A computational approach of data smoothening and prediction of diabetes dataset

Shivani Jakhmola, Tribikram Pradhan

Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 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 language	English
Title of host publication	Proceeding of the 3rd International Symposium on Women in Computing and Informatics, WCI 2015
Editors	Sushmita Mitra, Punam Bedi, Suzanne McIntosh, Rajasree M.S., Indu Nair
Publisher	Association for Computing Machinery (ACM)
Pages	744-748
Number of pages	5
Volume	10-13-August-2015
ISBN (Electronic)	9781450333610
DOIs	https://doi.org/10.1145/2791405.2791572
Publication status	Published - 10-08-2015
Event	3rd International Symposium on Women in Computing and Informatics, WCI 2015 - Kerala, India Duration: 10-08-2015 → 13-08-2015

Publication series

Name	ACM International Conference Proceeding Series
Volume	10-13-August-2015

Conference

Conference	3rd International Symposium on Women in Computing and Informatics, WCI 2015
Country/Territory	India
City	Kerala
Period	10-08-15 → 13-08-15

All Science Journal Classification (ASJC) codes

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/2791405.2791572

Cite this

Jakhmola, S., & Pradhan, T. (2015). A computational approach of data smoothening and prediction of diabetes dataset. In S. Mitra, P. Bedi, S. McIntosh, R. M.S., & I. Nair (Eds.), Proceeding of the 3rd International Symposium on Women in Computing and Informatics, WCI 2015 (Vol. 10-13-August-2015, pp. 744-748). (ACM International Conference Proceeding Series; Vol. 10-13-August-2015). 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. editor / Sushmita Mitra ; Punam Bedi ; Suzanne McIntosh ; Rajasree M.S. ; Indu Nair. Vol. 10-13-August-2015 Association for Computing Machinery (ACM), 2015. pp. 744-748 (ACM International Conference Proceeding Series).

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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.",

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Jakhmola, S & Pradhan, T 2015, A computational approach of data smoothening and prediction of diabetes dataset. in S Mitra, P Bedi, S McIntosh, R M.S. & I Nair (eds), Proceeding of the 3rd International Symposium on Women in Computing and Informatics, WCI 2015. vol. 10-13-August-2015, ACM International Conference Proceeding Series, 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. ed. / Sushmita Mitra; Punam Bedi; Suzanne McIntosh; Rajasree M.S.; Indu Nair. Vol. 10-13-August-2015 Association for Computing Machinery (ACM), 2015. p. 744-748 (ACM International Conference Proceeding Series; Vol. 10-13-August-2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

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Jakhmola S, Pradhan T. A computational approach of data smoothening and prediction of diabetes dataset. In Mitra S, Bedi P, McIntosh S, M.S. R, Nair I, editors, 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. (ACM International Conference Proceeding Series). doi: 10.1145/2791405.2791572

A computational approach of data smoothening and prediction of diabetes dataset

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All Science Journal Classification (ASJC) codes

UN SDGs

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