Integration of Internet of Things with Golden Eagle Optimization of Naive Bayes Classifier for Advanced Healthcare Monitoring System

Parashiva Murthy B M; Prabhakara Rao T; Vikas B; Venkatesh Sharma K; Souptik Sen; Ramesh Krishnamaneni

doi:10.55938/ijgasr.v5i1.340

Authors

Parashiva Murthy B M JSS Science and Technology University
Prabhakara Rao T Aditya University
Vikas B Sreenidhi University
Venkatesh Sharma K CVR College Of Engineering
Souptik Sen Prince Sultan University
Ramesh Krishnamaneni IBM (United States)

DOI:

https://doi.org/10.55938/ijgasr.v5i1.340

Keywords:

Advanced Healthcare Monitoring System, Golden Eagle Optimization, Mean Curvature Flow, Na¨ıve Bayes, Term frequency-inverse document frequency

Abstract

In the modern world, many things contribute to an unhealthy way of life for people, including irregular eating patterns, a diet deficiency in nutrition, environmental pollution, improper exercise, long hours at the office, restlessness, and elevated stress level. In a hospital setting, the most challenging tasks are attacking vulnerability and privacy. The issues of excessive consumption of energy, computational complexity cost, high traffic load, and low fault tolerance still exist despite the numerous solutions. This manuscript proposes the IoT-integrated machine learning approach for an advanced healthcare monitoring system. The IoT sensors are gathered via the Kaggle dataset. The data is pre-processed using Mean Curvature Flow (MCF) method to remove training errors, faults, and missing information while improving input data quality. The feature is extracted utilizing the Term frequency-inverse document frequency (TF-IDF). Then, the extracted features are transferred to the Naïve Bayes (NB) classifier for predicting patient health status. Additionally, the Golden Eagle Optimization (GEO) method is used to optimize the weight parameters of the NB. The proposed method implements and the efficiency of the proposed NB-GEO-based advanced healthcare monitoring system are assessed using numerous evaluation metrics via Precision, f-score, Sensitivity, accuracy, specificity, AUC, and computational time.

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