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Impact of Swarm Intelligence Techniques in Diabetes Disease Risk Prediction

Impact of Swarm Intelligence Techniques in Diabetes Disease Risk Prediction

Sushruta Mishra, Brojo Kishore Mishra, Soumya Sahoo, Bijayalaxmi Panda
Copyright: © 2016 |Volume: 6 |Issue: 2 |Pages: 15
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781466691384|DOI: 10.4018/IJKDB.2016070103
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MLA

Mishra, Sushruta, et al. "Impact of Swarm Intelligence Techniques in Diabetes Disease Risk Prediction." IJKDB vol.6, no.2 2016: pp.29-43. http://doi.org/10.4018/IJKDB.2016070103

APA

Mishra, S., Mishra, B. K., Sahoo, S., & Panda, B. (2016). Impact of Swarm Intelligence Techniques in Diabetes Disease Risk Prediction. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 6(2), 29-43. http://doi.org/10.4018/IJKDB.2016070103

Chicago

Mishra, Sushruta, et al. "Impact of Swarm Intelligence Techniques in Diabetes Disease Risk Prediction," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 6, no.2: 29-43. http://doi.org/10.4018/IJKDB.2016070103

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Abstract

Diabetes has affected over 246 million people worldwide and by 2025 it is expected to rise to over 380 million. With the rise of information technology and its continued advent into the medical and healthcare sector, different symptoms of diabetes are being documented. The techniques inspired from the distributed collective behavior of social colonies have shown worth and excellence in dealing with complex optimization problems and are becoming more popular nowadays. It can be used as an effective problem solving tool for identifying diabetes disease risks. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in data through various swarm optimization techniques by employing Support Vector Machines and Naïve Bayes algorithms. It proposes a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.

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