Machine Learning-Based Techniques of Anomaly Detection - As Applied to Health and Welfare Data

Oh, Miae; Park, Ah-yeon; Jin, Jaehyun

Full metadata record

DC Field	Value
dc.contributor.author	Oh, Miae
dc.contributor.author	Park, Ah-yeon
dc.contributor.author	Jin, Jaehyun
dc.date.accessioned	2019-12-04T00:27:31Z
dc.date.available	2019-12-04T00:27:31Z
dc.date.issued	2019
dc.identifier.isbn	9788968275951
dc.identifier.uri	https://repository.kihasa.re.kr/handle/201002/33480
dc.description.abstract	Machine learning is a subfield of the growing research on artificial intelligence (AI). Specifically, it focuses on the development of algorithms and technology that enable computers to learn independently using data. Machine learning, widely regarded as instrumental in advancements in image processing, video and voice recognition, and Internet searches, has proven to be quite effective as a tool for anomaly prediction and detection. Anomaly detection refers to the process by which one finds instances or data, out of a given pool, that diverge from expected patterns. In this study, we define the concept of anomaly detection, after which we apply the anomaly detection methodology to the given sets of health and welfare policy data to perform an exploratory analysis. We discuss the issues involved in the application of anomaly detection and summarize the policy implications. The data subjected to our analysis include the fludeoxyglucose positron emission tomography (FDG-PET) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), regarding health, and the results of the Elderly Survey 2017.
dc.description.tableOfContents	Ⅰ. Introduction 1 Ⅱ. Conceptualization 7 1. Anomaly Detection: Definition and Conceptualization 9 2. Factors of Anomaly Detection 11 Ⅲ. Exploratory Analysis of Anomaly Detection in Health Data 15 1. Anomaly Detection in FDG-PET Data for Early Diagnosis of Alzheimer’s Disease 17 2. Analysis Overview 18 3. Analysis Outcomes 20 4. Implications 24 Ⅳ. Exploratory Analysis of Anomaly Detection in Welfare Data 27 1. Data Overview 31 2. Defining Anomalies 32 3. Exploratory Data Analysis 33 4. Implications 42 Ⅴ. Conclusion 45 References 51
dc.format	application/pdf
dc.format	image/jpeg
dc.format.extent	57
dc.language	eng
dc.publisher	한국보건사회연구원
dc.publisher	Korea Institute for Health and Social Affairs
dc.rights	Attribution-NonCommercial-NoDerivs 2.0 Korea (CC BY-NC-ND 2.0 KR)
dc.rights	KOGL BY-NC-ND
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/2.0/kr/
dc.rights.uri	http://www.kogl.or.kr/info/licenseType4.do
dc.title	Machine Learning-Based Techniques of Anomaly Detection - As Applied to Health and Welfare Data
dc.type	Book
dc.type.local	Report
dc.subject.keyword	Machine Learning;Anomaly Detection;Exploratory Data Analysis
dc.type.other	정책현안자료
dc.identifier.localId	Policy Report 2019-08
dc.date.dateaccepted	2019-12-04T00:27:31Z
dc.date.datesubmitted	2019-12-04T00:27:31Z
dc.type.research	정책
dc.type.nkis	기본연구보고서
dc.subject.kihasa	보건복지 정보화