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2018 | OriginalPaper | Buchkapitel

Clustering and Classification of Effective Diabetes Diagnosis: Computational Intelligence Techniques Using PCA with kNN

verfasst von : Nimmala Mangathayaru, B. Mathura Bai, Panigrahi Srikanth

Erschienen in: Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1

Verlag: Springer International Publishing

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Abstract

The fourth leading disease in the world today is Diabetes and there are number of challenges to predict and identify the disease. Data mining proposes effective approaches to identify the diabetic patients. This paper proposes clustering and classification of effective diabetes diagnosis based on computational intelligence techniques using PCA with kNN. Diabetes disease data is used to identify feature of clusters. Diabetes disease diagnosis proposes novel distribution function applied to classify each patient. This proposed procedure defines clusters and similarity measure based on classifying with each cluster using computational intelligence techniques. PCA using diabetes disease data for dimensionality reduction. Novel similarity measure is proposed in kNN for classification. Accuracy measures are computed for each patient.

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Vorheriges Kapitel Statistical Study to Prove Importance of Causal Relationship Extraction in Rare Class Classification
Nächstes Kapitel A Comprehensive Survey and Open Challenges of Mining Bigdata
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Metadaten
Titel
Clustering and Classification of Effective Diabetes Diagnosis: Computational Intelligence Techniques Using PCA with kNN
verfasst von
Nimmala Mangathayaru
B. Mathura Bai
Panigrahi Srikanth
Copyright-Jahr
2018
Buch
Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1

Print ISBN: 978-3-319-63672-6

Electronic ISBN: 978-3-319-63673-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-63673-3_52