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An Efficient Diagnosis System for Detection of Liver Disease Using a Novel Integrated Method Based on Principal Component Analysis and K-Nearest Neighbor (PCA-KNN)

An Efficient Diagnosis System for Detection of Liver Disease Using a Novel Integrated Method Based on Principal Component Analysis and K-Nearest Neighbor (PCA-KNN)

Aman Singh, Babita Pandey
Copyright: © 2016 |Volume: 11 |Issue: 4 |Pages: 14
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781466689633|DOI: 10.4018/IJHISI.2016100103
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MLA

Singh, Aman, and Babita Pandey. "An Efficient Diagnosis System for Detection of Liver Disease Using a Novel Integrated Method Based on Principal Component Analysis and K-Nearest Neighbor (PCA-KNN)." IJHISI vol.11, no.4 2016: pp.56-69. http://doi.org/10.4018/IJHISI.2016100103

APA

Singh, A. & Pandey, B. (2016). An Efficient Diagnosis System for Detection of Liver Disease Using a Novel Integrated Method Based on Principal Component Analysis and K-Nearest Neighbor (PCA-KNN). International Journal of Healthcare Information Systems and Informatics (IJHISI), 11(4), 56-69. http://doi.org/10.4018/IJHISI.2016100103

Chicago

Singh, Aman, and Babita Pandey. "An Efficient Diagnosis System for Detection of Liver Disease Using a Novel Integrated Method Based on Principal Component Analysis and K-Nearest Neighbor (PCA-KNN)," International Journal of Healthcare Information Systems and Informatics (IJHISI) 11, no.4: 56-69. http://doi.org/10.4018/IJHISI.2016100103

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Abstract

Talking about organ failure and people immediately recall kidney diseases. On the contrary, there is no such alertness about liver diseases and its failure despite the fact that this disease is one of the leading causes of mortality worldwide. Therefore, an effective diagnosis and in time treatment of patients is paramount. This study accordingly aims to construct an intelligent diagnosis system which integrates principle component analysis (PCA) and k-nearest neighbor (KNN) methods to examine the liver patient dataset. The model works with the combination of feature extraction and classification performed by PCA and KNN respectively. Prediction results of the proposed system are compared using statistical parameters that include accuracy, sensitivity, specificity, positive predictive value and negative predictive value. In addition to higher accuracy rates, the model also attained remarkable sensitivity and specificity, which were a challenging task given an uneven variance among attribute values in the dataset.

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