Data mining techniques for analyzing healthcare conditions of urban space-person lung using meta-heuristic optimized neural networks
Source of Publication
© 2020, Springer Science+Business Media, LLC, part of Springer Nature. 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.
Air pollution, Harmony optimized modular neural network, Health issues, Hilbert–Schmidt independence criterion, Meta-heuristic optimized neural networks, Urban computing
Abugabah, Ahed; AlZubi, Ahmad Ali; Al-Obeidat, Feras; Alarifi, Abdulaziz; and Alwadain, Ayed, "Data mining techniques for analyzing healthcare conditions of urban space-person lung using meta-heuristic optimized neural networks" (2020). All Works. 1159.
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