Publication Type : Journal Article
Publisher : IGI Global
Source : Smart Medical Data Sensing and IoT Systems Design in Healthcare, 48-75
Campus : Amritapuri
School : School of Computing
Center : AI (Artificial Intelligence) and Distributed Systems
Department : Computer Science and Engineering
Year : 2020
Abstract : Nearest neighbor algorithms like kNN and Parzen Window are generative algorithms that are used extensively for medical diagnosis and classification of diseases. The data generated or collected in healthcare is high dimensional and cannot be assumed to follow a particular distribution. The conventional approaches fail due to computational complexity, curse of dimensionality, and varying distributions. Hence, this chapter deals with a blending technique for evaluation of nearest neighbor algorithms based on various parameters such as the size of data, dimensions of data, window size, and number of nearest neighbors to make it suitable for massive datasets. Dimensionality reduction and clustering are combined with nearest neighbor classifier such as kNN and Parzen Window to observe the performance of the blended models on various types of datasets. Experimental results on 15 real datasets with various models reveal the efficacy of the proposed blends.
Cite this Research Publication : S Harikumar, "Blended Models for Nearest Neighbour Algorithms for High Dimensional Smart Medical Data", Smart Medical Data Sensing and IoT Systems Design in Healthcare, 48-75, 2020