A Knowledge Extraction Pipeline between Supervised and Unsupervised Machine Learning Using Gaussian Mixture Models for Anomaly Detection

Chefira, Reda; Rakrak, Said

doi:10.5626/jcse.2021.15.1.1

Cited by 4 publications

(1 citation statement)

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“…Further, it can be difficult to estimate the number of clusters, which requires large datasets [79]. The gaussian mixture model is used in [81] for anomaly detection.…”

Section: Gaussian Mixture Modelmentioning

confidence: 99%

A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges

Rahman

Zhou

et al. 2023

Sensors

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Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).

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“…Further, it can be difficult to estimate the number of clusters, which requires large datasets [79]. The gaussian mixture model is used in [81] for anomaly detection.…”

Section: Gaussian Mixture Modelmentioning

confidence: 99%