The Application of Data Mining

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The Application of Data Mining in Health
Research
Li Xiaosong, M.D., M.P.H., Ph.D. Prof. of
Biostatistics School of Public Health Sichuan
University
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Knowledge discovery in databases ( KDD)

• With the rapid development of the Information
  Industry, great advances have been made in data
  production and collection capacities,however, the
  conflict of rich data but pool knowledge is
  getting increasingly evident.
• It is the Knowledge discovery in databases
  that cater the demand!

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Data mining
Data tomb?
Knowledge
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Data Mining (DM)

• In the face of vast databases, how to discover
  the hidden but
• useful knowledge from data, which can help in
  the government
• and enterprises decision-making, so as to
  get more benefit had
• become an important problem to solve

DM
Data Knowledge

• Data Mining is the procedure of distilling the
  unknown but
• potentially valuable information and
  knowledge from plentiful
• data which is uncompleted, misty and
  stochastic

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Data Mining A KDD Process
Knowledge

• Data mining--the core of knowledge discovery
  process

Pattern Evaluation
Data Mining
Task-relevant Data
Selection
Data Warehouse
Data Cleaning
Data Integration
Databases
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Classification of Data Mining technology

• Association Rule Mining
• Classification and Predicting
• Clustering Analysis
• Trend Analysis
• Patten Analysis

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The Application of Data Mining
Banking Telecom Economy
Meteorology Agriculture Health care
Military
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Data Mining applied in health care

• The application of data mining in medical and
  health
• researches had prove itself to be effective,
  showing
• great development potentialities
• Now data mining has become a key method in
• obtaining information in clinical medicine,
  biomedicine,
• pharmacy and public health

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Data Mining applied in Clinical Research

• Finding the relationship among diseases
• Searching the rule of disease development and
  prevalence
• Disease diagnosis and treatment
• Summarizing therapeutic effects
• e.g. using Bayes classification decision tree
  
• classification in disease diagnosis

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Data Mining applied in Biomedicine
-A powerful tool in DNA analysis!

• Using Sequence Model Analysis Similarity
  Retrieval
• to find the gene sequence model for certain
  kind of diseases
• Data Cleaning Data integration is valuable in
  the data
• integration and database building of gene
• Association Rules Analysis can help to discover
  Gene
• crossover and correlation in a Genome

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Data Mining applied in Public Health

• Spatio-temporal Data Mining used in infectious
  diseases
• monitoring to search for the epidemic rules
  and distribution
• characteristics of diseases
• Using Time Series Analysis, Neural Networks to
  predict the
• incidence and infant mortality rate of
  infectious diseases
• Association Rules to discuss the influencing
  factors of
• diseases and health seeking behavior
• Clustering and classification are now widely
  used in the
• decision support system for health insurance

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e.g. Application of Association Rules in Medical
Data Analysis

• Association Rules aims to find out the
  relationship among valuables in database,
  resulting in deferent types of rules

Table 1original data from a research on heart
disease
LAD- The percentage of heat disease caused by
left anterior descending
coronary artery RCA- The percentage of heat
disease caused by right coronary
artery
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Results
Table.2 Medical Association Rules

• Rule 1 indicates40 of the cases are
  male, over 70 years old and
• have the habit of smoking, the possibility
  of RCA50 is 100
• Rule 2 indicates20 of the cases
  are female, under 70 years old and
• have the habit of smoking, the possibility of
  LAD70 is 100

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The future application of Data Mining in medical
research

• Data Mining is based on a series of new data
  process technology
• Wavelets Analysis
• Neural Networks
• Genetic Algorithm
• Fuzzy Logic Reasoning

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Challenges facing Data Mining

• The data of medical research are always
  complicated and
• unique in types and structures
• To integrate the specialized knowledge of both
  medical and data-
• processing staffs
• Plenty of data and repeatedly practices are
  needed
• Targets
• To form a real useful data mining system
  for
• health research

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Backpropagation Neural Networks (BPNNs)

• A type of artificial neural network
• A way to model highly complex, nonlinear
  solutions to classification problems
• Useful in classifying health-related phenomena

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e.g. Classification of smoking cessation status
with BPNN

• Classifier performance estimates
• The confidence intervals of Az for both the BPNN
  and logistic classifiers are narrow
• And exceeds random chance (Az0.5) by at least
  25 points
• The finding indicates the performance of both
  classifiers exceeds that of random
• chance
• Az area under receiver operating
  characteristic curve

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e.g. Classification of smoking cessation status
with BPNN
Binormal conventional ROC curves for BPNN and
logistic regression classifiers

• The graph illustrates the estimated true
  positive fraction (TPF) at multiple values of the
  false positive fraction (FPF)
• The areas under the ROC curve differ at a0.05
  

ROCreceiver operating characteristic
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• Thank you!