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Data mining techniques for analyzing healthcare conditions of urban space-person lung using meta-heuristic optimized neural networks

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Abstract

Urban computing is one of the effective fields that have ability to collect the large volume of data, integrate and analyze the data in urban space. The urban space faces several issues such as traffic congestion, more energy consumption, air pollution and so on. Among the several problems, air pollution is one of the major issues because it creates several health issues. So, this paper introduces the meta-heuristic optimized neural network to analyze patient health to predict different diseases. Initially, patient data are collected, normalized by applying a min–max normalization process. Then different features are extracted and Hilbert–Schmidt Independence Criterion based features are selected. Further patient's health condition is analyzed and classified into a normal and abnormal person. The classification process is done by applying the harmony optimized modular neural network. Here the system efficiency is evaluated using simulation results, which ensures maximum accuracy of 98.9% -ELT-COPD and 98% -NIH clinical dataset.

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Acknowledgements

This work was supported in part the Deanship of Scientific Research at King Saud University for funding this work through research group No. (RG-1439-53). This work was supported in part by Zayed University, office of research under Grant No. R17089.

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Authors and Affiliations

  1. College of Technological Innovation, Zayed University, Dubai, United Arab Emirates

    Ahed Abugabah & Feras Al-Obeidat

  2. Computer Science Department, King Saud University, Riyadh, Saudi Arabia

    Ahmad Ali AlZubi, Abdulaziz Alarifi & Ayed Alwadain

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  1. Ahed Abugabah
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  2. Ahmad Ali AlZubi
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Abugabah, A., AlZubi, A.A., Al-Obeidat, F. et al. Data mining techniques for analyzing healthcare conditions of urban space-person lung using meta-heuristic optimized neural networks. Cluster Comput 23, 1781–1794 (2020). https://doi.org/10.1007/s10586-020-03127-w

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