Abstract
Neural networks are extensively used nowadays to carry out tasks like prediction, pattern matching, and detections primarily carried out by medical practitioners acquiring expertise and knowledge of a very high level in the area of medical science. The artificial neural networks (ANNs) are applied to conduct tasks like image analysis and interpretation, signal analysis, development of drugs, etc. The proposed work focuses on performing effectual prediction and diagnosis of diseases like diabetes using data classification techniques, thereby modifying these algorithms to enhance their performance. This proposed work contains simulated learning vector quantization algorithm, and replaced the conventional Euclidean distance function with the Canberra distance function. The simulation results revealed significant performance enhancement in the output produced by this modification. The modification is also applied to K-means algorithm and its outcome is recorded.
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Shirish Nagar, Ajay Khunteta (2016). A Proposed Modification Over Learning Vector Quantization and K-Means Algorithms for Performance Enhancement. In: Afzalpulkar, N., Srivastava, V., Singh, G., Bhatnagar, D. (eds) Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2638-3_75
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DOI: https://doi.org/10.1007/978-81-322-2638-3_75
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