Disease Prediction Through Syndromes by Clustering Algorithm

Raihana Zannat; Shammir Hossain; Shakawat Al Sakib; Sumaya Akter; Khadija Tut Tahera; Ohidujjaman

doi:doi:10.11648/j.ajeit.20210502.15

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Disease Prediction Through Syndromes by Clustering Algorithm

Raihana Zannat, Shammir Hossain, Shakawat Al Sakib, Sumaya Akter, Khadija Tut Tahera, Ohidujjaman

Published in American Journal of Education and Information Technology (Volume 5, Issue 2)

Received: 25 September 2021 Accepted: 12 October 2021 Published: 28 October 2021

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Abstract

There are numerous machine learning methods that capable to develop smart automated algorithms to examine high-dimensional and multi-modal biomedical dataset. This paper emphases on through clustering algorithms to advance revealing and analysis of human diseases. The mass population’s disease assessment was not ever been familiar and nevertheless is an intricate procedure and necessitates a great level of competence. Numerous assessment support methods established encouraging diagnostic representations however merely an insufficient have been properly estimated in clinical surroundings. Moreover stand-alone decision support systems rely on profoundly on a massive volume of dataset. This research deploy unsupervised clustering approach such as K-means algorithm to build a proficient system to recognize human diseases by assessing syndromes to progress the superiority of health issues. The medical professionals and practitioners can use this smart system to corroborate the diseases diagnosis. The study is significant in health sector to reduce all kinds of diagnosis expenses.

Published in	American Journal of Education and Information Technology (Volume 5, Issue 2)
DOI	10.11648/j.ajeit.20210502.15
Page(s)	93-96
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Clustering Approach, Data Mining, K-means, Machine Learning, Symptoms

References

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[2]	Shadab Adam Pattekari and Asma Parveen, “Prediction System For Heart Disease Using Naïve Bayes”, International Journal of Advanced Computer and Mathematical Sciences ISSN 2230-9624. Vol 3, Issue 3, 2012, pp 290-294.
[3]	S. Vijayarani and S. Sudha, “An Efficient Clustering Algorithm for Predicting Diseases from Hemogram Blood Test Samples”, Indian Journal of Science and Technology, vol. 8, pp. 1–8, Aug. 2015.
[4]	Rebecca Hermon and Patricia A H Williams, “Big data in healthcare: What is it used for?” 3rd Australian eHealth Informatics and Security Conference. Held on the 1-3 December, 2014 at Edith Cowan University, Joondalup Campus, Perth, Western Australia.
[5]	Jyoti Soni and et al., “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction”, International Journal of Computer Applications, vol. 17, pp. 43–48, Mar. 2011.
[6]	K Rajalakshmi, S. S. Dhenakaran and N Roobin “Comparative Analysis of K-Means Algorithm in Disease Prediction”, International Journal of Science, Engineering and Technology Research, vol. 4, pp. 2697–2699, Jul. 2015.
[7]	B. P. Shantakumar and Y. S. Kumaraswamy “Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network” European Journal of Scientific Research ISSN 1450-216X Vol. 31 No. 4 (2009), pp. 642-656.
[8]	B. Sundar V, T. Devi and N. Saravanan,”Development of a Data Clustering Algorithm for Predicting Heart”, International Journal of Computer Applications (0975 – 888) Vol. 48– No. 7, 2012.
[9]	M. Umamaheswari and P. Isakki Devi, “Prediction of myocardial infarction using K-medoid clustering algorithm”, IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 2017.
[10]	S. Shinde and B. Tidke, “Improved K-means Algorithm for searching Research Papers”, International Journal of Computer Science & Communication networks, ISSN: 2249-5789, Vol. 4 (6), 197-202.
[11]	Z. Muhammad, R. Imam, P. Yudi, “Log Classification using K-means Clustering for Identify Internet User Behaviours”, International Journal of Compiler Applications, (0975-8887), Vol. 154-No. 3, November 2016.

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APA Style

Raihana Zannat, Shammir Hossain, Shakawat Al Sakib, Sumaya Akter, Khadija Tut Tahera, et al. (2021). Disease Prediction Through Syndromes by Clustering Algorithm. American Journal of Education and Information Technology, 5(2), 93-96. https://doi.org/10.11648/j.ajeit.20210502.15

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ACS Style

Raihana Zannat; Shammir Hossain; Shakawat Al Sakib; Sumaya Akter; Khadija Tut Tahera, et al. Disease Prediction Through Syndromes by Clustering Algorithm. Am. J. Educ. Inf. Technol. 2021, 5(2), 93-96. doi: 10.11648/j.ajeit.20210502.15

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AMA Style

Raihana Zannat, Shammir Hossain, Shakawat Al Sakib, Sumaya Akter, Khadija Tut Tahera, et al. Disease Prediction Through Syndromes by Clustering Algorithm. Am J Educ Inf Technol. 2021;5(2):93-96. doi: 10.11648/j.ajeit.20210502.15

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@article{10.11648/j.ajeit.20210502.15,
  author = {Raihana Zannat and Shammir Hossain and Shakawat Al Sakib and Sumaya Akter and Khadija Tut Tahera and Ohidujjaman},
  title = {Disease Prediction Through Syndromes by Clustering Algorithm},
  journal = {American Journal of Education and Information Technology},
  volume = {5},
  number = {2},
  pages = {93-96},
  doi = {10.11648/j.ajeit.20210502.15},
  url = {https://doi.org/10.11648/j.ajeit.20210502.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajeit.20210502.15},
  abstract = {There are numerous machine learning methods that capable to develop smart automated algorithms to examine high-dimensional and multi-modal biomedical dataset. This paper emphases on through clustering algorithms to advance revealing and analysis of human diseases. The mass population’s disease assessment was not ever been familiar and nevertheless is an intricate procedure and necessitates a great level of competence. Numerous assessment support methods established encouraging diagnostic representations however merely an insufficient have been properly estimated in clinical surroundings. Moreover stand-alone decision support systems rely on profoundly on a massive volume of dataset. This research deploy unsupervised clustering approach such as K-means algorithm to build a proficient system to recognize human diseases by assessing syndromes to progress the superiority of health issues. The medical professionals and practitioners can use this smart system to corroborate the diseases diagnosis. The study is significant in health sector to reduce all kinds of diagnosis expenses.},
 year = {2021}
}

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TY  - JOUR
T1  - Disease Prediction Through Syndromes by Clustering Algorithm
AU  - Raihana Zannat
AU  - Shammir Hossain
AU  - Shakawat Al Sakib
AU  - Sumaya Akter
AU  - Khadija Tut Tahera
AU  - Ohidujjaman
Y1  - 2021/10/28
PY  - 2021
N1  - https://doi.org/10.11648/j.ajeit.20210502.15
DO  - 10.11648/j.ajeit.20210502.15
T2  - American Journal of Education and Information Technology
JF  - American Journal of Education and Information Technology
JO  - American Journal of Education and Information Technology
SP  - 93
EP  - 96
PB  - Science Publishing Group
SN  - 2994-712X
UR  - https://doi.org/10.11648/j.ajeit.20210502.15
AB  - There are numerous machine learning methods that capable to develop smart automated algorithms to examine high-dimensional and multi-modal biomedical dataset. This paper emphases on through clustering algorithms to advance revealing and analysis of human diseases. The mass population’s disease assessment was not ever been familiar and nevertheless is an intricate procedure and necessitates a great level of competence. Numerous assessment support methods established encouraging diagnostic representations however merely an insufficient have been properly estimated in clinical surroundings. Moreover stand-alone decision support systems rely on profoundly on a massive volume of dataset. This research deploy unsupervised clustering approach such as K-means algorithm to build a proficient system to recognize human diseases by assessing syndromes to progress the superiority of health issues. The medical professionals and practitioners can use this smart system to corroborate the diseases diagnosis. The study is significant in health sector to reduce all kinds of diagnosis expenses.
VL  - 5
IS  - 2
ER  -

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Author Information

Raihana Zannat

Department Software Engineering, Daffodil International University, Dhaka, Bangladesh
Shammir Hossain

Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
Shakawat Al Sakib

Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
Sumaya Akter

Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
Khadija Tut Tahera

Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
Ohidujjaman

Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh

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Plain Text BibTeX RIS

APA Style

Raihana Zannat, Shammir Hossain, Shakawat Al Sakib, Sumaya Akter, Khadija Tut Tahera, et al. (2021). Disease Prediction Through Syndromes by Clustering Algorithm. American Journal of Education and Information Technology, 5(2), 93-96. https://doi.org/10.11648/j.ajeit.20210502.15

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ACS Style

Raihana Zannat; Shammir Hossain; Shakawat Al Sakib; Sumaya Akter; Khadija Tut Tahera, et al. Disease Prediction Through Syndromes by Clustering Algorithm. Am. J. Educ. Inf. Technol. 2021, 5(2), 93-96. doi: 10.11648/j.ajeit.20210502.15

Copy | Download

AMA Style

Raihana Zannat, Shammir Hossain, Shakawat Al Sakib, Sumaya Akter, Khadija Tut Tahera, et al. Disease Prediction Through Syndromes by Clustering Algorithm. Am J Educ Inf Technol. 2021;5(2):93-96. doi: 10.11648/j.ajeit.20210502.15

Copy | Download

@article{10.11648/j.ajeit.20210502.15,
  author = {Raihana Zannat and Shammir Hossain and Shakawat Al Sakib and Sumaya Akter and Khadija Tut Tahera and Ohidujjaman},
  title = {Disease Prediction Through Syndromes by Clustering Algorithm},
  journal = {American Journal of Education and Information Technology},
  volume = {5},
  number = {2},
  pages = {93-96},
  doi = {10.11648/j.ajeit.20210502.15},
  url = {https://doi.org/10.11648/j.ajeit.20210502.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajeit.20210502.15},
  abstract = {There are numerous machine learning methods that capable to develop smart automated algorithms to examine high-dimensional and multi-modal biomedical dataset. This paper emphases on through clustering algorithms to advance revealing and analysis of human diseases. The mass population’s disease assessment was not ever been familiar and nevertheless is an intricate procedure and necessitates a great level of competence. Numerous assessment support methods established encouraging diagnostic representations however merely an insufficient have been properly estimated in clinical surroundings. Moreover stand-alone decision support systems rely on profoundly on a massive volume of dataset. This research deploy unsupervised clustering approach such as K-means algorithm to build a proficient system to recognize human diseases by assessing syndromes to progress the superiority of health issues. The medical professionals and practitioners can use this smart system to corroborate the diseases diagnosis. The study is significant in health sector to reduce all kinds of diagnosis expenses.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Disease Prediction Through Syndromes by Clustering Algorithm
AU  - Raihana Zannat
AU  - Shammir Hossain
AU  - Shakawat Al Sakib
AU  - Sumaya Akter
AU  - Khadija Tut Tahera
AU  - Ohidujjaman
Y1  - 2021/10/28
PY  - 2021
N1  - https://doi.org/10.11648/j.ajeit.20210502.15
DO  - 10.11648/j.ajeit.20210502.15
T2  - American Journal of Education and Information Technology
JF  - American Journal of Education and Information Technology
JO  - American Journal of Education and Information Technology
SP  - 93
EP  - 96
PB  - Science Publishing Group
SN  - 2994-712X
UR  - https://doi.org/10.11648/j.ajeit.20210502.15
AB  - There are numerous machine learning methods that capable to develop smart automated algorithms to examine high-dimensional and multi-modal biomedical dataset. This paper emphases on through clustering algorithms to advance revealing and analysis of human diseases. The mass population’s disease assessment was not ever been familiar and nevertheless is an intricate procedure and necessitates a great level of competence. Numerous assessment support methods established encouraging diagnostic representations however merely an insufficient have been properly estimated in clinical surroundings. Moreover stand-alone decision support systems rely on profoundly on a massive volume of dataset. This research deploy unsupervised clustering approach such as K-means algorithm to build a proficient system to recognize human diseases by assessing syndromes to progress the superiority of health issues. The medical professionals and practitioners can use this smart system to corroborate the diseases diagnosis. The study is significant in health sector to reduce all kinds of diagnosis expenses.
VL  - 5
IS  - 2
ER  -

Copy | Download