Data mining is a powerful method to extract knowledge from data. Raw data faces various challenges that make traditional method improper for knowledge extraction. Data mining is supposed to be able to handle various data types in all formats. Medical data mining is a multidisciplinary field with contribution of medicine and data mining. each paper is studied based on the six medical tasks: screening, diagnosis, treatment, prognosis, monitoring and management.

1. A SEMINAR ON
KNOWLEDGE DISCOVERY IN MEDICINE
: CURRENT ISSUES AND FUTURE TREND
Guided by: Presented by:
Prof. B.R. Bombade Avinash M. Hanwate
2016MNS013

2. CONTENTS
• INTRODUCTION
• MOTIVATION OF STUDY
• DATA MINING AND MEDICAL DATA MINING
• REVIEW AND ANALYSIS
• CURRENT ISSUES AND FUTURE TRENDS
• CONCLUSION
• REFERENCES

3. INTRODUCTION
• Data mining is a powerful method to extract knowledge from data.
Raw data faces various challenges that make traditional method
improper for knowledge extraction.
• Data mining is supposed to be able to handle various data types in
all formats.
• Medical data mining is a multidisciplinary field with contribution
of medicine and data mining.
• each paper is studied based on the six medical tasks: screening,
diagnosis, treatment, prognosis, monitoring and management.
• In each task, five data mining approaches are considered:
classification, regression, clustering, association and hybrid.

4. MOTIVATION OF STUDY
• Workflow applied for extraction of medical data mining papers:
• Contains four processes:
1. Broad search
2. Refine search
3. Extract basic concepts
4. Analyze
Fig. Workflow to collect related papers with the aim of provide this
survey

5. • Previous reviews in context of knowledge extraction in medicine:
• We can consider each of these review papers from two points of
view:
1. Algorithm
2. Medicine
• Scope of this study:
• The study of review papers in previous section shows that
application of knowledge extraction from medicine is started
from last decade and therefore it is located in a teenage period.

6. DATA MINING AND MEDICAL
DATA MINING
• Data mining:
Data mining process divided into three phase: data pre-processing,
data modeling, and data post-processing.
• Data Pre-processing:
• Data description and summarization:
• Data Cleaning
• Data Integration
• Attribute Transformation
• Discretization and Binarization
• Data Reduction

7. Fig. Main data pre-processing approaches

8. DATA MODELING:
• All the tasks in data modeling step can be divided into predictive and
descriptive categories. Predictive algorithms are divided into
classification and regression based on type of target variable (discrete
or continuous).
• Descriptive algorithms are also classified as clustering and
association rule mining.
Fig. Process of classification.

9. DATA POST-PROCESSING:
• visualization and evaluation of extracted knowledge is taken into
account.
• Visualization is stated as the essential matter in data mining. It is
considered as an advantage for an algorithm.
• There are various predictive and descriptive algorithms for
knowledge extraction.

10. MEDICAL DATA MINING:
• Definition and goal
• Extraction of implicit, potentially useful and novel
information from medical data to improve accuracy, decrease
time and cost, construct decision support system with the aim
of health promotion.
• This definition contains three parts:
1. Data mining
2. Medical nature
3. Goals

11. MEDICAL DATA:
• Medical data is one of the most difficult data type for mining.
• This type of data is generated by several sources such as medical
examinations, imaging and tests. We need to select an appropriate
data mining algorithm regarding the type of data.
• In this study only structural data are considered and other types
such as text are not considered.

12. • In Fig. frequency of using each data types in literature are shown.
Fig. Percentage of medical data types that used by literature.

13. Fig. Medical data challenges.

14. MEDICAL NATURE CHALLENGES:
• High risk:
• Medicine can be known as high risk due to the association
with human life.
• Medical activities outcome is death or life and this is
different from engineering view such as optimization.
Mistake or failure can lead to punishment.
• Interaction with a wide variety of users:
• Patient
• Physician
• Nurse
• Health care system

15. MEDICAL DATA MINING ADAPTIVE
STANDARD FRAMEWORK
• Problem understanding
• Data understanding
• Data pre-processing
• Data processing
• Data post-processing
• Deployment
Fig. Medical data mining adaptive standard framework.

16. MEDICAL DATA MINING TREND
• In Fig. medical data mining trend is presented and compared with
data mining.
Fig. Medical data mining trend compared with data mining.

17. REVIEW AND ANALYSIS
• All activities in medicine can be divided into six domains that we
know as ‘‘medical tasks’’:
1. Screening
2. Diagnosis
3. Treatment
4. Prognosis
5. Monitoring
6. Management

18. CURRENT ISSUES
• Twelve important issues in medical data mining are considered:
1. Medical data mining goals
2. Types of data
3. Integration
4. Scalability
5. Medical data mining challenges
6. Users
7. Medical tasks
8. Diseases
9. Data pre-processing
10. Data modeling
11. Classification
12. Data post-processing

19. FUTURE TREND
• The decision tree is known as the most popular algorithm in medical
data mining, its usage has decreased in recent years.
• In contrast, SVM has been more used in recent works.
• Hybrid approaches try to combine two or more algorithms with the
aim of enhancing performance.
• Three approaches have appeared to be combined with data mining
algorithms: fuzzy, expert system and evolutionary algorithms.
• Fuzzy approach has more attention to construct hybrid models.
• Hence, SVM and fuzzy approach are expected to be popular methods
in the future.

20. CONCLUSION
• In this study, we focused only on a structural data.
• Therefore as for future works, provide a review on application of
text mining in medicine is recommended.
• Also, Introducing the tools, package and successful implemented
programs in this field is proposed.
• Finally we can conclude this paper around three points:
• Based on six medical tasks
• Based on disease
• Based on data mining

21. REFERENCES
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Shearer, C., & Wirth, R.(2000). CRISP-DM 1.0 step-by-step data
mining guide. In The CRISP-DM consortium.
2] Organization, W. H. (2007). World health statistics 2007: WORLD
HEALTH ORGN.
3] Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data
mining techniques in customer relationship management: A literature
review and classification.Expert Systems with Applications, 36,
2592–2602.
4] Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data
mining to knowledge discovery in databases. AI Magazine, 17, 37–
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