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학교환경에서의 ADHD 아동 활동 패턴 분석 및 데이터 마이닝 기법을 이용한 ADHD 선별모델 개발
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 박래웅 | - |
| dc.contributor.author | Kam, Hye Jin | - |
| dc.date.accessioned | 2019-10-21T07:17:44Z | - |
| dc.date.available | 2019-10-21T07:17:44Z | - |
| dc.date.issued | 2011-02 | - |
| dc.identifier.other | 11227 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/17893 | - |
| dc.description | 학위논문(박사)--아주대학교 일반대학원 :의학과,2011. 2 | - |
| dc.description.tableofcontents | I. INTRODUCTION 1 A. BACKGROUNDS 1 B. STUDY AIMS 8 II. MATERIALS AND METHODS 9 A. STUDY OVERVIEW 9 B. PARTICIPANTS 11 1. RECRUITMENTS & QUESTIONNAIRES ADMINISTRATION 11 2. PARTICIPANTS SELECTION WITH CLINICAL DIAGNOSIS 12 C. ELEMENTARY SCHOOL IN KOREA 14 D. ACTIVITY MEASUREMENT & INFORMATION ACUISITION 15 E. COMPARISONS OF SITUATIONAL EFFECT ON ACTIVITY PATTERNS 17 1. OVERVIEW 17 2. FEATURE EXTRACTION 19 3. PINCIPAL COMPONENT ANALYSIS (PCA) 23 4. STATISTICAL ANALYSIS 25 F. MODEL CONSTRUCTION FOR ADHD SCREENING 26 1. OVERVIEW OF MODEL CONSTRUCTION 26 2. FEATURE EXTRACTION & SELECTION 28 3. DECISION TREE MODEL CONSTRUCTION 32 4. SOLVING CLASS IMBALANCE PROBLEM 36 5. MODEL EVALUATION & STATISTICAL ANALYSIS 38 III. RESULTS 44 A. COMPARISONS OF ACTIVITY PATTERNS 44 1. PARTICIPANT SELECTION 44 2. PCA ANALYSIS 46 3. STATISTICAL ANALYSIS 48 B. SCREENING MODEL CONSTRUCTION 65 1. PARTICIPANT SELECTION 65 2. SELECTE ACTIVITY FEATURES 67 3. CONSTRUCTED SCREENING MODELS 69 4. COMPARISON WITH SELECTED FEATURES 73 5. MODEL EVALUATION 75 IV. DISCUSSION 78 A. COMPARISONS OF SITUATIONAL EFFECT ON ACTIVITY PATTERNS 78 1. ACTIVITY DISTRIBUTION 78 2. THE EFFECTS OF SITUATIONAL DEMANDS INVOLVING RESCTRICTIONS 80 B. MODEL CONSTRUCTION FOR ADHD SCREENING 82 1. SCREENING MODELS FOR ADHD BY MONITORING CHILDREN’S ACTIVITY 82 2. SIGNIFICANCE OF SELECTED FEATURES 86 3. APPLICABILITY 88 C. LIMITATIONS 90 V. CONCLUSION 94 REFERENCES 96 국문요약 105 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 학교환경에서의 ADHD 아동 활동 패턴 분석 및 데이터 마이닝 기법을 이용한 ADHD 선별모델 개발 | - |
| dc.title.alternative | Analysis of Activity Pattern of Children Attending Elementary School, and Development of a Prediction Model Based on Data Mining Approach for ADHD Screening | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | Hye Jin Kam | - |
| dc.contributor.department | 일반대학원 의학과 | - |
| dc.date.awarded | 2011. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 569353 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000011227 | - |
| dc.subject.keyword | ADHD | - |
| dc.subject.keyword | Classroom behavior | - |
| dc.subject.keyword | Activity monitoring | - |
| dc.subject.keyword | Activity level | - |
| dc.subject.keyword | Activities of daily living | - |
| dc.subject.keyword | Actigraph | - |
| dc.subject.keyword | Decision support | - |
| dc.description.alternativeAbstract | Questionnaire-based attention deficit disorder with hyperactivity (ADHD) screening tests may not always be objective or accurate, owing to both subjectivity and prejudice. Despite attempts to develop objective measures to characterize ADHD, no widely applicable index currently exists. The principal aim of this study was to determine whether high-resolution activity features could provide a sufficient analytical foundation for determining the activity transitions of children with ADHD and to develop a decision support model for ADHD screening by monitoring children’s school activities using a 3-axial actigraph. Actigraphs were placed on the non-dominant wrists of 153 children for 3 hours, while they were at school. Children who scored high on the questionnaires were clinically examined by child psychiatrists, who then confirmed ADHD. Mean, variance, and ratios of activity within partitioned activity regions (a unit of 0.1G) were extracted as activity features. As a primary research on the effect by contexts of school life, activity features were extracted for three courses, including art, language and math. And, they were compared between the ADHD and non-ADHD groups via principal component analysis (PCA) and other statistical analyses. For, screening model construction, two decision tree models were constructed using the C5.0 algorithm after feature selection step: [A] from whole hours (class + playtime) and [B] during classes. Accuracy, sensitivity, and specificity were evaluated. Positive/negative predictive value, odds ratio, relative risk, likelihood ratio and area under ROC curve (AUC) were also calculated for model evaluation. In the comparison analysis on course contents, especially in the art course, the two groups of children were almost completely separable by the new features, and the activity distributions of the groups differed significantly over a broad range. Those findings also showed that the course contents appeared to influence the activity patterns of children with ADHD. Monitoring the actual magnitude and counts of activity over a broad range could facilitate deeper investigations into the distributions or patterns of activities. And, two 5-depth decision trees were constructed by C5.0 algorithms. [Model A] three non-ADHD children were misclassified, resulting in an accuracy score of 97.89%. Sensitivity and NPV were 1.00. Specificity and PPV were 0.98 and 0.58-0.77, respectively. [Model B] 11 non-ADHD children were misclassified, resulting in an accuracy score of 92.23%. Specificity and PPV were scored at 0.92 and 0.27-0.47, respectively. Objective screening of latent ADHD patients can be accomplished with a simple watch-like sensor, which is worn for just a few hours while the child attends school. The model proposed herein can be applied to a great many children without heavy cost in time and manpower cost, and would generate valuable results from a public health perspective. | - |
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