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Feature selection and classification in supporting report based self-management with chronic pain

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This document discusses using machine learning techniques to analyze self-reported data from people with chronic pain and identify their health status. It evaluates different feature selection methods and classification algorithms to determine an optimal approach for supporting self-management. The best performing method was found to be a multilayer perceptron classifier with high accuracy and area under the ROC curve, suggesting it could effectively classify health status levels from the self-reported data.

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Feature Selection and Classification in Supporting Report-Based Self- Management for People with Chronic Pain Author:Yan Huang, Huiru Zheng, Chris Nugent, Paul McCullagh, Norman Black, Kevin E. Vowles, and Lance McCracken Source: Information Technology in Biomedicine, IEEE Transactions on Jan. 2011, Journals & Magazines Advisor: Ben-Jye Chang Student: YU-HSIEN CHO
OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches VI. Numerical results V. Conclusion
Introduction Older people has increased, that two thirds of people who reached retirement age had at least two chronic conditions.
Introduction Machine learning approach, self-reporting data collected from the integrated biopsychosocial treatment, in order to identify an optimal set of features for supporting self management.
Introduction Fig. 1. Assessment interface of the PSMS and the assessment workflow for self-management
Introduction We assess the feasibility of applying automated classification techniques to identify "low" and "better" health status levels from self-reporting data and explore an appropriate classification algorithm.
OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches VI. Numerical results V. Conclusion
Issue Numbers of selected questions and classification performance of a persons health status level. Which ranking method and which classification model had the best performance.
OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches VI. Numerical results V. Conclusion
Motivations Traditional health care, expensive, consuming significant resources , inconvenient. PWCP, self-management of their health care has been shown to be effective in terms of improving their QoL. PWCP: People With Chronic Pain QoL: Quality of Life
OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches VI. Numerical results V. Conclusion
Approachs A.Dataset 187 subjects who suffered from chronic pain 8 types of questionnaire total number of questions was 329, answers had values "pretreatment stage as " low health level , "posttreatment stage as " better health level 16 (8.6%) of the patients withdrew , 171 (91.4%) of the patients completed the treatment training sets:114 patients, testing sets:57 patients
Approachs B.Methods Four feature selection methods, rank the questions. 1.SVM-RFE(Support Vector Machine With Recursive Feature Elimination): The ranking criterion for feature i : Methods: Step 1: Train an SVM on the dataset. Step 2: Rank features according to the criterion c. Step 3: Eliminate the lowest ranked feature. Step 4: If more than one feature remains, return to step 1.
Approachs Q.1 2.OneR: 1-level decision tree 1 21 2 16 Steps: 3 22 For each feature fi For each value v from the domain of fi 4 20 Select the set of instances where feature fi has value v 5 35 Let c = the most frequent class in that set Add the clause if feature fi has value v then the class is cto the rule for feature fi Output the rule with the highest classification accuracy.
Approachs 3.Information Gain:based on Shannons information theory and can be calculated from (1)(3) A represents a feature (question) of an instance, which has n values two classes(pre. and post.),each has 114 instances
Approachs
Approachs 4. X2 Statistic: m, number of answers for one question(feature) ni , frequency of that answer i Pi , probability of that answer i n , total frequency for all the questions answers, 228329
Approachs C. Classification Performance Assessment Purpose : classify the persons appropriate health status Classifier: C4.5,Naive Bayes, SVM, MLP
Approachs 1.Overall accuracy: 2.Area Under the ROC Curve(AUC): Suggested as a tool, which can evaluate the performance of the classification alorgithm
OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches VI. Numerical results V. Conclusion
Numerical results There were no significant differences between the feature ranking methods in overall classification accuracy. (any of the four feature ranking methods can be used) There were significant differences between the classifiers for each ranking method. The MLP classifier has been identified as the best option to build the classification model for PSMS in the sense that both overall accuracy and AUC were very high.
OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches V. Numerical results IV. Conclusion
Conclusion Feedback information for their self- management Changing their behavior,lifestyle, and care plan in order to achieve effective self- management of their chronic condition
Feature selection and classification in supporting report based self-management with chronic pain

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Feature selection and classification in supporting report based self-management with chronic pain

  • 1. Feature Selection and Classification in Supporting Report-Based Self- Management for People with Chronic Pain Author:Yan Huang, Huiru Zheng, Chris Nugent, Paul McCullagh, Norman Black, Kevin E. Vowles, and Lance McCracken Source: Information Technology in Biomedicine, IEEE Transactions on Jan. 2011, Journals & Magazines Advisor: Ben-Jye Chang Student: YU-HSIEN CHO
  • 2. OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches VI. Numerical results V. Conclusion
  • 3. Introduction Older people has increased, that two thirds of people who reached retirement age had at least two chronic conditions.
  • 4. Introduction Machine learning approach, self-reporting data collected from the integrated biopsychosocial treatment, in order to identify an optimal set of features for supporting self management.
  • 5. Introduction Fig. 1. Assessment interface of the PSMS and the assessment workflow for self-management
  • 6. Introduction We assess the feasibility of applying automated classification techniques to identify "low" and "better" health status levels from self-reporting data and explore an appropriate classification algorithm.
  • 7. OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches VI. Numerical results V. Conclusion
  • 8. Issue Numbers of selected questions and classification performance of a persons health status level. Which ranking method and which classification model had the best performance.
  • 9. OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches VI. Numerical results V. Conclusion
  • 10. Motivations Traditional health care, expensive, consuming significant resources , inconvenient. PWCP, self-management of their health care has been shown to be effective in terms of improving their QoL. PWCP: People With Chronic Pain QoL: Quality of Life
  • 11. OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches VI. Numerical results V. Conclusion
  • 12. Approachs A.Dataset 187 subjects who suffered from chronic pain 8 types of questionnaire total number of questions was 329, answers had values "pretreatment stage as " low health level , "posttreatment stage as " better health level 16 (8.6%) of the patients withdrew , 171 (91.4%) of the patients completed the treatment training sets:114 patients, testing sets:57 patients
  • 13. Approachs B.Methods Four feature selection methods, rank the questions. 1.SVM-RFE(Support Vector Machine With Recursive Feature Elimination): The ranking criterion for feature i : Methods: Step 1: Train an SVM on the dataset. Step 2: Rank features according to the criterion c. Step 3: Eliminate the lowest ranked feature. Step 4: If more than one feature remains, return to step 1.
  • 14. Approachs Q.1 2.OneR: 1-level decision tree 1 21 2 16 Steps: 3 22 For each feature fi For each value v from the domain of fi 4 20 Select the set of instances where feature fi has value v 5 35 Let c = the most frequent class in that set Add the clause if feature fi has value v then the class is cto the rule for feature fi Output the rule with the highest classification accuracy.
  • 15. Approachs 3.Information Gain:based on Shannons information theory and can be calculated from (1)(3) A represents a feature (question) of an instance, which has n values two classes(pre. and post.),each has 114 instances
  • 17. Approachs 4. X2 Statistic: m, number of answers for one question(feature) ni , frequency of that answer i Pi , probability of that answer i n , total frequency for all the questions answers, 228329
  • 18. Approachs C. Classification Performance Assessment Purpose : classify the persons appropriate health status Classifier: C4.5,Naive Bayes, SVM, MLP
  • 19. Approachs 1.Overall accuracy: 2.Area Under the ROC Curve(AUC): Suggested as a tool, which can evaluate the performance of the classification alorgithm
  • 20. OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches VI. Numerical results V. Conclusion
  • 21. Numerical results There were no significant differences between the feature ranking methods in overall classification accuracy. (any of the four feature ranking methods can be used) There were significant differences between the classifiers for each ranking method. The MLP classifier has been identified as the best option to build the classification model for PSMS in the sense that both overall accuracy and AUC were very high.
  • 22. OUTLINE I. Introduction II. Issue III. Motivations VI. Approaches V. Numerical results IV. Conclusion
  • 23. Conclusion Feedback information for their self- management Changing their behavior,lifestyle, and care plan in order to achieve effective self- management of their chronic condition