Anomalies Detecting in Medical Metrics Using Machine Learning Tools

Melnykova, Nataliia; Kulievych, Roman; Vycluk, Yaroslav; Melnykova, Kateryna; Melnykov, Volodymyr

doi:10.1016/j.procs.2021.12.312

Cited by 7 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It reduced overfitting problems in decision trees and variances so that it substantially improves the accuracy in the terminated comparison. Importantly, the RFM evidence portrays that the LCM research belongs to machine learning-based medical anomaly detection that aims to predict and diagnose illnesses [ 34 ]. The RFM, therefore, is a generally advanced application of emergent disease detection.…”

Section: Discussionmentioning

confidence: 99%

Determining Association between Lung Cancer Mortality Worldwide and Risk Factors Using Fuzzy Inference Modeling and Random Forest Modeling

Denise

Zhan

et al. 2022

IJERPH

View full text Add to dashboard Cite

Lung cancer remains the leading cause for cancer mortality worldwide. While it is well-known that smoking is an avoidable high-risk factor for lung cancer, it is necessary to identify the extent to which other modified risk factors might further affect the cell’s genetic predisposition for lung cancer susceptibility, and the spreading of carcinogens in various geographical zones. This study aims to examine the association between lung cancer mortality (LCM) and major risk factors. We used Fuzzy Inference Modeling (FIM) and Random Forest Modeling (RFM) approaches to analyze LCM and its possible links to 30 risk factors in 100 countries over the period from 2006 to 2016. Analysis results suggest that in addition to smoking, low physical activity, child wasting, low birth weight due to short gestation, iron deficiency, diet low in nuts and seeds, vitamin A deficiency, low bone mineral density, air pollution, and a diet high in sodium are potential risk factors associated with LCM. This study demonstrates the usefulness of two approaches for multi-factor analysis of determining risk factors associated with cancer mortality.

show abstract

Section: Discussionmentioning

confidence: 99%

Determining Association between Lung Cancer Mortality Worldwide and Risk Factors Using Fuzzy Inference Modeling and Random Forest Modeling

Denise

Zhan

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…Many times, the detection algorithms are paired with traditional detection systems, usually rule-based, for better performance. In this regard, there are various application domains where these types of algorithms are used [6]: network security for intrusion detection (used for behavior analysis in enterprise settings for known and novel threats), surveillance for suspicious moves and actions (via visual and audio capture systems) [23], [24], detection of fraudulent transactions in banking industry (including transactions involving digital goods) [25], [26], energy optimization in smart buildings [27], medical smart equipment (capable of identification and analysis of anomalies to assist in medical diagnosis) [28], [29], and, generally, in usecases where anomalous states can appear infrequently enough in the operating processes to be properly treated, but pose enough dangers to warrant such a system.…”

Section: B Methods and Algorithms In Anomaly Detectionmentioning

confidence: 99%

A Comparative Study of Unsupervised Anomaly Detection Algorithms used in a Small and Medium-Sized Enterprise

Petrariu¹,

Moscaliuc²,

Turcu³

et al. 2022

IJACSA

View full text Add to dashboard Cite

Anomaly detection finds application in several industries and domains. The anomaly detection market is growing driven by the increasing development and dynamic adoption of emerging technologies. Depending on the type of supervision, there are three main types of anomaly detection techniques: unsupervised, semi-supervised, and supervised. Given the wide variety of available anomaly detection algorithms, how can one choose which approach is most appropriate for a particular application? The purpose of this evaluation is to compare the performance of five unsupervised anomaly detection algorithms applied to a specific dataset from a small and medium-sized software enterprise, presented in this paper. To reduce the cost and complexity of a system developed to solve the problem of anomaly detection, a solution is to use machine learning (ML) algorithms that are available in one of the open-source libraries, such as the scikit-learn library or the PyOD library. These algorithms can be easily and quickly integrated into a low-cost software application developed to meet the needs of a small and medium-sized enterprise (SME). In our experiments, we considered some unsupervised algorithms available in PyOD library. The obtained results are presented, alongside with the limitations of the research.

show abstract

“…Software for artificial intelligence that supports Hypertext Markup Language (HTML) learning models purely based on the neocortex's neurobiology [37].…”

Section: Nupicmentioning

confidence: 99%

Maximizing a Farm Yield Through Precision Agriculture utilizing Fourth Industrial Revolution (4IR) Tools and Space Technology

Ngongoma¹,

Musasa²,

Moloi³

2023

Preprint

View full text Add to dashboard Cite

The globe and more particularly the economically developed regions of the world are currently in the era of the fourth Industrial revolution (4IR). Conversely; the economically developing regions in the world and more particularly the African continent have not yet even fully passed through the Third Industrial Revolution (3IR) wave and its economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth of the global human population which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet up with the steep food demand and stabilize the economy of the African continent and countries like India whose economy is mainly dependent on Agriculture. The tools we have at our disposal to utilize in the digitization of farming practices include space technology and Global Navigation and Satellite System (GNSS) in particular, Machine learning (ML), precision agriculture and communication systems such as the Internet of Things (IoT) and Information And Communication Technologies (ICT). The most pressing challenges in the farming field include the monitoring of diseases, pests, weeds and nutrient deficiencies in the crops as early detection translates to swift and timely correction actions and hence more yield at the end of a farming cycle. Vast opportunities in the field of precision agriculture still exist that can amount to further research studies such as the lack of real-time monitoring and real-time corrective action focus.

show abstract

Anomalies Detecting in Medical Metrics Using Machine Learning Tools

Cited by 7 publications

References 11 publications

Determining Association between Lung Cancer Mortality Worldwide and Risk Factors Using Fuzzy Inference Modeling and Random Forest Modeling

Determining Association between Lung Cancer Mortality Worldwide and Risk Factors Using Fuzzy Inference Modeling and Random Forest Modeling

A Comparative Study of Unsupervised Anomaly Detection Algorithms used in a Small and Medium-Sized Enterprise

Maximizing a Farm Yield Through Precision Agriculture utilizing Fourth Industrial Revolution (4IR) Tools and Space Technology

Contact Info

Product

Resources

About