Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| DATA MINING in HEALTHCARE SYSTEMS | SSMY1169710 | Fall Semester | 3+0 | 3 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | Second Cycle (Master's Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Erman GEDİKLİ |
| Name of Lecturer(s) | Assist.Prof. Kevser Banu KÖSE |
| Assistant(s) | As.Ress.Yaşar Gökalp |
| Aim | To recognize the basic concepts and methods of data mining, interpret and apply common data mining methods including clustering and classification, design a data mining model for a given problem, and apply, and interpret the main clinical and managerial decision support systems in healthcare. |
| Course Content | This course contains; Introduction to Data Mining ,Data Mining Process,Data Discovery and Visualization,Feature Selection and Data Transformation,Clustering Methods,Exercise: Clustering,Classification Methods - Decision Trees,Exercise: Classifications,Exercise: Classifications,Association Rule Mining,Exercise: Association rule mining,Exercise: Problem oriented data mining,Midterm project presentations,Midterm project presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Explain the basic concepts and processes in data mining | 16, 18, 9 | A |
| Define data mining and its objectives | 16, 9 | A |
| Explain basic concepts, including data, database, data warehouse, etc | 16, 9 | A |
| Describe the data mining process. | 16, 9 | A |
| Explain common data mining methods | 13, 14, 16, 18, 6, 8, 9 | A |
| Explains classification and clustering method | 16, 9 | A |
| Distinguish the appropriate method for a given data mining problem. | 10, 13, 16, 4, 6, 9 | A |
| Distinguish the differences between descriptive and predictive methods | 16, 6, 9 | A |
| Propose a correct (supervised/unsupervised) method for a given data mining problem | 16, 6, 9 | A, G |
| Interpret the most possible methods for a given data set with respect to the data types and pattern | 16, 6, 9 | A |
| Explain the clinical and management decision support systems. | 14, 18, 4, 5, 6, 9 | A |
| Explain the knowledge-based DSSs | 16, 9 | A |
| Explain the learning-based DSSs | 16, 9 | A |
| Explain the data mining and its sub-processes | 16, 9 | A |
| Use an open source data mining tool (KNIME) | 14, 16, 2, 4, 5, 6, 8, 9 | E, F |
| Apply the data preprocess method with KNIME | 12, 16, 18, 8, 9 | E, F |
| Use the clustering methods with KNIME | 16, 4, 8, 9 | A, E, F |
| Use the classification methods with KNIME | 16, 8, 9 | A, E |
| Use the association rules with KNIME | 16, 6, 9 | A |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 16: Question - Answer Technique, 18: Micro Teaching Technique, 2: Project Based Learning Model, 4: Inquiry-Based Learning, 5: Cooperative Learning, 6: Experiential Learning, 8: Flipped Classroom Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to Data Mining | Basic database concepts |
| 2 | Data Mining Process | The usage of SQL as DML; TSQL; data warehouse architectures, the reasons yield data manipulation |
| 3 | Data Discovery and Visualization | The graph types in data visualization and the components of a graph, such as dimension, measure, etc. |
| 4 | Feature Selection and Data Transformation | Data types, generalization, specialization of data |
| 5 | Clustering Methods | Main clustering approaches, such as Hierachical Clustering, Centroid-based Clustering, Density-based Clustering, Distribution-based Clustering. |
| 6 | Exercise: Clustering | The excercises with KNIME for especially clustering |
| 7 | Classification Methods - Decision Trees | The main differences between clustering and clalssifications, how to generate a decision tree and the main decision tree algorithms, such as ID 3 and C4.5 |
| 8 | Exercise: Classifications | The excercises with KNIME for especially classification |
| 9 | Exercise: Classifications | The excercises with KNIME for especially classification |
| 10 | Association Rule Mining | Market box analysis |
| 11 | Exercise: Association rule mining | The excercises with KNIME for especially ARM |
| 12 | Exercise: Problem oriented data mining | Clustering, decision trees, association rule mining |
| 13 | Midterm project presentations | A data mining solution having feature selection, data transformation, data mining application and evaluation of the result |
| 14 | Midterm project presentations | A data mining solution having feature selection, data transformation, data mining application and evaluation of the result |
| Resources |
| Lecture notes and lab sheets (will be shared regularly on the lecture pages) - Veri Madenciliği Teori Uygulama ve Felsefesi, Dr. İlker KÖSE (2015) - Kavram ve Algoritmalarıyla Temel Veri Madenciliği, Dr. Gökhan SİLAHTAROĞLU - Veri Madenciliği Yöntemleri, Dr. Yalçın ÖZKAN - Han Jiawei and Kamber Micheline (2006), Data Mining: Concepts and Techniques, Morgan Kaufmann Publisher San Francisco - Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley, (2005) |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Develop and deepen knowledge in the same or in a different field to the proficiency level based on Bachelor level qualifications. | X | |||||
| 2 | Conceive the interdisciplinary interaction which the field is related with. | X | |||||
| 3 | Use of theoretical and practical knowledge within the field at a proficiency level and solve the problem faced related to the field by using research methods. | X | |||||
| 4 | Interpret the knowledge about the field by integrating the information gathered from different disciplines and formulate new knowledge. | X | |||||
| 5 | Independently conduct studies that require proficiency in the field. | X | |||||
| 6 | Take responsibility and develop new strategic solutions as a team member in order to solve unexpected complex problems faced within the applications in the field. | X | |||||
| 7 | Evaluate knowledge and skills acquired at proficiency level in the field with a critical approach and direct the learning. | X | |||||
| 8 | Investigate, improve social connections and their conducting norms with a critical view and act to change them when necessary. Communicate with peers by using a foreign language at least at a level of European Language Portfolio B2 General Level. | ||||||
| 9 | Define the social and environmental aspects of engineering applications. | X | |||||
| 10 | Audit the data gathering, interpretation, implementation and announcement stages by taking into consideration the cultural, scientific, and ethic values and teach these values. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 3 | 42 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 2 | 10 | 20 | |||
| Term Project | 14 | 3 | 42 | |||
| Presentation of Project / Seminar | 1 | 20 | 20 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 1 | 50 | 50 | |||
| General Exam | 1 | 60 | 60 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 234 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(234/30) | 8 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| DATA MINING in HEALTHCARE SYSTEMS | SSMY1169710 | Fall Semester | 3+0 | 3 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | Second Cycle (Master's Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Erman GEDİKLİ |
| Name of Lecturer(s) | Assist.Prof. Kevser Banu KÖSE |
| Assistant(s) | As.Ress.Yaşar Gökalp |
| Aim | To recognize the basic concepts and methods of data mining, interpret and apply common data mining methods including clustering and classification, design a data mining model for a given problem, and apply, and interpret the main clinical and managerial decision support systems in healthcare. |
| Course Content | This course contains; Introduction to Data Mining ,Data Mining Process,Data Discovery and Visualization,Feature Selection and Data Transformation,Clustering Methods,Exercise: Clustering,Classification Methods - Decision Trees,Exercise: Classifications,Exercise: Classifications,Association Rule Mining,Exercise: Association rule mining,Exercise: Problem oriented data mining,Midterm project presentations,Midterm project presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Explain the basic concepts and processes in data mining | 16, 18, 9 | A |
| Define data mining and its objectives | 16, 9 | A |
| Explain basic concepts, including data, database, data warehouse, etc | 16, 9 | A |
| Describe the data mining process. | 16, 9 | A |
| Explain common data mining methods | 13, 14, 16, 18, 6, 8, 9 | A |
| Explains classification and clustering method | 16, 9 | A |
| Distinguish the appropriate method for a given data mining problem. | 10, 13, 16, 4, 6, 9 | A |
| Distinguish the differences between descriptive and predictive methods | 16, 6, 9 | A |
| Propose a correct (supervised/unsupervised) method for a given data mining problem | 16, 6, 9 | A, G |
| Interpret the most possible methods for a given data set with respect to the data types and pattern | 16, 6, 9 | A |
| Explain the clinical and management decision support systems. | 14, 18, 4, 5, 6, 9 | A |
| Explain the knowledge-based DSSs | 16, 9 | A |
| Explain the learning-based DSSs | 16, 9 | A |
| Explain the data mining and its sub-processes | 16, 9 | A |
| Use an open source data mining tool (KNIME) | 14, 16, 2, 4, 5, 6, 8, 9 | E, F |
| Apply the data preprocess method with KNIME | 12, 16, 18, 8, 9 | E, F |
| Use the clustering methods with KNIME | 16, 4, 8, 9 | A, E, F |
| Use the classification methods with KNIME | 16, 8, 9 | A, E |
| Use the association rules with KNIME | 16, 6, 9 | A |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 16: Question - Answer Technique, 18: Micro Teaching Technique, 2: Project Based Learning Model, 4: Inquiry-Based Learning, 5: Cooperative Learning, 6: Experiential Learning, 8: Flipped Classroom Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction to Data Mining | Basic database concepts |
| 2 | Data Mining Process | The usage of SQL as DML; TSQL; data warehouse architectures, the reasons yield data manipulation |
| 3 | Data Discovery and Visualization | The graph types in data visualization and the components of a graph, such as dimension, measure, etc. |
| 4 | Feature Selection and Data Transformation | Data types, generalization, specialization of data |
| 5 | Clustering Methods | Main clustering approaches, such as Hierachical Clustering, Centroid-based Clustering, Density-based Clustering, Distribution-based Clustering. |
| 6 | Exercise: Clustering | The excercises with KNIME for especially clustering |
| 7 | Classification Methods - Decision Trees | The main differences between clustering and clalssifications, how to generate a decision tree and the main decision tree algorithms, such as ID 3 and C4.5 |
| 8 | Exercise: Classifications | The excercises with KNIME for especially classification |
| 9 | Exercise: Classifications | The excercises with KNIME for especially classification |
| 10 | Association Rule Mining | Market box analysis |
| 11 | Exercise: Association rule mining | The excercises with KNIME for especially ARM |
| 12 | Exercise: Problem oriented data mining | Clustering, decision trees, association rule mining |
| 13 | Midterm project presentations | A data mining solution having feature selection, data transformation, data mining application and evaluation of the result |
| 14 | Midterm project presentations | A data mining solution having feature selection, data transformation, data mining application and evaluation of the result |
| Resources |
| Lecture notes and lab sheets (will be shared regularly on the lecture pages) - Veri Madenciliği Teori Uygulama ve Felsefesi, Dr. İlker KÖSE (2015) - Kavram ve Algoritmalarıyla Temel Veri Madenciliği, Dr. Gökhan SİLAHTAROĞLU - Veri Madenciliği Yöntemleri, Dr. Yalçın ÖZKAN - Han Jiawei and Kamber Micheline (2006), Data Mining: Concepts and Techniques, Morgan Kaufmann Publisher San Francisco - Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley, (2005) |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Develop and deepen knowledge in the same or in a different field to the proficiency level based on Bachelor level qualifications. | X | |||||
| 2 | Conceive the interdisciplinary interaction which the field is related with. | X | |||||
| 3 | Use of theoretical and practical knowledge within the field at a proficiency level and solve the problem faced related to the field by using research methods. | X | |||||
| 4 | Interpret the knowledge about the field by integrating the information gathered from different disciplines and formulate new knowledge. | X | |||||
| 5 | Independently conduct studies that require proficiency in the field. | X | |||||
| 6 | Take responsibility and develop new strategic solutions as a team member in order to solve unexpected complex problems faced within the applications in the field. | X | |||||
| 7 | Evaluate knowledge and skills acquired at proficiency level in the field with a critical approach and direct the learning. | X | |||||
| 8 | Investigate, improve social connections and their conducting norms with a critical view and act to change them when necessary. Communicate with peers by using a foreign language at least at a level of European Language Portfolio B2 General Level. | ||||||
| 9 | Define the social and environmental aspects of engineering applications. | X | |||||
| 10 | Audit the data gathering, interpretation, implementation and announcement stages by taking into consideration the cultural, scientific, and ethic values and teach these values. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |