Download presentation

Presentation is loading. Please wait.

Published byAlex Willow Modified over 2 years ago

1
1 CSE 300 Data mining and its application and usage in medicine By Radhika

2
2 CSE 300 Data Mining and Medicine History Past 20 years with relational databases More dimensions to database queries earliest and most successful area of data mining Mid 1800s in London hit by infectious disease Two theories –Miasma theory Bad air propagated disease –Germ theory Water-borne Advantages –Discover trends even when we don’t understand reasons –Discover irrelevant patterns that confuse than enlighten –Protection against unaided human inference of patterns provide quantifiable measures and aid human judgment Data Mining Patterns persistent and meaningful Knowledge Discovery of Data

3
3 CSE 300 The future of data mining 10 biggest killers in the US Data mining = Process of discovery of interesting, meaningful and actionable patterns hidden in large amounts of data

4
4 CSE 300 Major Issues in Medical Data Mining Heterogeneity of medical data Volume and complexity Physician’s interpretation Poor mathematical categorization Canonical Form Solution: Standard vocabularies, interfaces between different sources of data integrations, design of electronic patient records Ethical, Legal and Social Issues Data Ownership Lawsuits Privacy and Security of Human Data Expected benefits Administrative Issues

5
5 CSE 300 Why Data Preprocessing? Patient records consist of clinical, lab parameters, results of particular investigations, specific to tasks Incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data Noisy: containing errors or outliers Inconsistent: containing discrepancies in codes or names Temporal chronic diseases parameters No quality data, no quality mining results! Data warehouse needs consistent integration of quality data Medical Domain, to handle incomplete, inconsistent or noisy data, need people with domain knowledge

6
6 CSE 300 What is Data Mining? The KDD Process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation

7
7 CSE 300 From Tables and Spreadsheets to Data Cubes A data warehouse is based on a multidimensional data model that views data in the form of a data cube A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year) Fact table contains measures (such as dollars_sold) and keys to each of related dimension tables W. H. Inmon: “ A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management ’ s decision-making process. ”

8
8 CSE 300 Data Warehouse vs. Heterogeneous DBMS Data warehouse: update-driven, high performance Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis Do not contain most current information Query processing does not interfere with processing at local sources Store and integrate historical information Support complex multidimensional queries

9
9 CSE 300 Data Warehouse vs. Operational DBMS OLTP (on-line transaction processing) Major task of traditional relational DBMS Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. OLAP (on-line analytical processing) Major task of data warehouse system Data analysis and decision making Distinct features (OLTP vs. OLAP): User and system orientation: customer vs. market Data contents: current, detailed vs. historical, consolidated Database design: ER + application vs. star + subject View: current, local vs. evolutionary, integrated Access patterns: update vs. read-only but complex queries

10
10 CSE 300

11
11 CSE 300 Why Separate Data Warehouse? High performance for both systems DBMS tuned for OLTP: access methods, indexing, concurrency control, recovery Warehouse tuned for OLAP: complex OLAP queries, multidimensional view, consolidation Different functions and different data: Missing data: Decision support requires historical data which operational DBs do not typically maintain Data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources Data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled

12
12 CSE 300

13
13 CSE 300

14
14 CSE 300 Typical OLAP Operations Roll up (drill-up): summarize data by climbing up hierarchy or by dimension reduction Drill down (roll down): reverse of roll-up from higher level summary to lower level summary or detailed data, or introducing new dimensions Slice and dice: project and select Pivot (rotate): reorient the cube, visualization, 3D to series of 2D planes. Other operations drill across: involving (across) more than one fact table drill through: through the bottom level of the cube to its back-end relational tables (using SQL)

15
15 CSE 300

16
16 CSE 300

17
17 CSE 300 Multi-Tiered Architecture Data Warehouse Extract Transform Load Refresh OLAP Engine Analysis Query Reports Data mining Monitor & Integrator Metadata Data Sources Front-End Tools Serve Data Marts Operational DBs other sources Data Storage OLAP Server

18
18 CSE 300 Steps of a KDD Process Learning the application domain: relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation: Find useful features, dimensionality/variable reduction, invariant representation. Choosing functions of data mining summarization, classification, regression, association, clustering. Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge

19
19 CSE 300 Common Techniques in Data Mining Predictive Data Mining Most important Classification: Relate one set of variables in data to response variables Regression: estimate some continuous value Descriptive Data Mining Clustering: Discovering groups of similar instances Association rule extraction Variables/Observations Summarization of group descriptions

20
20 CSE 300Leukemia Different types of cells look very similar Given a number of samples (patients) can we diagnose the disease accurately? Predict the outcome of treatment? Recommend best treatment based of previous treatments? Solution: Data mining on micro-array data 38 training patients, 34 testing patients ~ 7000 patient attributes 2 classes: Acute Lymphoblastic Leukemia(ALL) vs Acute Myeloid Leukemia (AML)

21
21 CSE 300 Clustering/Instance Based Learning Uses specific instances to perform classification than general IF THEN rules Nearest Neighbor classifier Most studied algorithms for medical purposes Clustering– Partitioning a data set into several groups (clusters) such that Homogeneity: Objects belonging to the same cluster are similar to each other Separation: Objects belonging to different clusters are dissimilar to each other. Three elements The set of objects The set of attributes Distance measure

22
22 CSE 300 Measure the Dissimilarity of Objects Find best matching instance Distance function Measure the dissimilarity between a pair of data objects Things to consider Usually very different for interval-scaled, boolean, nominal, ordinal and ratio-scaled variables Weights should be associated with different variables based on applications and data semantic Quality of a clustering result depends on both the distance measure adopted and its implementation

23
23 CSE 300 Minkowski Distance Minkowski distance: a generalization If q = 2, d is Euclidean distance If q = 1, d is Manhattan distance xixi xjxj q=2q=1 6 6 12 8.48 X i (1,7) X j (7,1)

24
24 CSE 300 Binary Variables A contingency table for binary data Simple matching coefficient Object i Object j

25
25 CSE 300 Dissimilarity between Binary Variables Example A1A2A3A4A5A6A7 Object 1 1011100 Object 2 1110001 Object 1 Object 210sum1224 0213 sum437

26
26 CSE 300 K-nearest neighbors algorithm Initialization Arbitrarily choose k objects as the initial cluster centers (centroids) Iteration until no change For each object O i Calculate the distances between O i and the k centroids (Re)assign O i to the cluster whose centroid is the closest to O i Update the cluster centroids based on current assignment

27
27 CSE 300 k-Means Clustering Method cluster mean current clusters new clusters objects relocated

28
28 CSE 300Dataset Data set from UCI repository http://kdd.ics.uci.edu/ http://kdd.ics.uci.edu/ 768 female Pima Indians evaluated for diabetes After data cleaning 392 data entries

29
29 CSE 300 Hierarchical Clustering Groups observations based on dissimilarity Compacts database into “labels” that represent the observations Measure of similarity/Dissimilarity Euclidean Distance Manhattan Distance Types of Clustering Single Link Average Link Complete Link

30
30 CSE 300 Hierarchical Clustering: Comparison Average-link Centroid distance 1 2 3 4 5 6 1 2 5 3 4 Single-linkComplete-link 1 2 3 4 5 6 1 2 5 3 4 1 2 3 4 5 6 1 2 5 3 4 1 2 3 4 5 6 1 2 3 4 5

31
31 CSE 300 Compare Dendrograms 1 2 5 3 6 4 2 5 3 6 4 1 Average-link Centroid distance Single-link Complete-link

32
32 CSE 300 Which Distance Measure is Better? Each method has both advantages and disadvantages; application-dependent Single-link Can find irregular-shaped clusters Sensitive to outliers Complete-link, Average-link, and Centroid distance Robust to outliers Tend to break large clusters Prefer spherical clusters

33
33 CSE 300 Dendrogram from dataset Minimum spanning tree through the observations Single observation that is last to join the cluster is patient whose blood pressure is at bottom quartile, skin thickness is at bottom quartile and BMI is in bottom half Insulin was however largest and she is 59-year old diabetic

34
34 CSE 300 Dendrogram from dataset Maximum dissimilarity between observations in one cluster when compared to another

35
35 CSE 300 Dendrogram from dataset Average dissimilarity between observations in one cluster when compared to another

36
36 CSE 300 Supervised versus Unsupervised Learning Supervised learning (classification) Supervision: Training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations New data is classified based on training set Unsupervised learning (clustering) Class labels of training data are unknown Given a set of measurements, observations, etc., need to establish existence of classes or clusters in data

37
37 CSE 300 Derive models that can use patient specific information, aid clinical decision making Apriori decision on predictors and variables to predict No method to find predictors that are not present in the data Numeric Response Least Squares Regression Categorical Response Classification trees Neural Networks Support Vector Machine Decision models Prognosis, Diagnosis and treatment planning Embed in clinical information systems Classification and Prediction

38
38 CSE 300 Least Squares Regression Find a linear function of predictor variables that minimize the sum of square difference with response Supervised learning technique Predict insulin in our dataset :glucose and BMI

39
39 CSE 300 Decision Trees Decision tree Each internal node tests an attribute Each branch corresponds to attribute value Each leaf node assigns a classification ID3 algorithm Based on training objects with known class labels to classify testing objects Rank attributes with information gain measure Minimal height least number of tests to classify an object Used in commercial tools eg: Clementine ASSISTANT Deal with medical datasets Incomplete data Discretize continuous variables Prune unreliable parts of tree Classify data

40
40 CSE 300 Decision Trees

41
41 CSE 300 Algorithm for Decision Tree Induction Basic algorithm (a greedy algorithm) Attributes are categorical (if continuous-valued, they are discretized in advance) Tree is constructed in a top-down recursive divide-and-conquer manner At start, all training examples are at the root Test attributes are selected on basis of a heuristic or statistical measure (e.g., information gain) Examples are partitioned recursively based on selected attributes

42
42 CSE 300 Training Dataset AgeBMIHereditaryVision Risk of Condition X P1<=30highnofairno P2<=30highnoexcellentno P3>40highnofairyes P4 31 … 40 mediumnofairyes P5 lowyesfairyes P6 lowyesexcellentno P7>40lowyesexcellentyes P8<=30mediumnofairno P9<=30lowyesfairyes P10 mediumyesfairyes P11<=30mediumyesexcellentyes P12>40mediumnoexcellentyes P13>40highyesfairyes P14 mediumnoexcellentno

43
43 CSE 300 Construction of A Decision Tree for “ Condition X ” Age? >40 30…40 <=30 [P1,…P14] Yes: 9, No:5 [P1,P2,P8,P9,P11] Yes: 2, No:3 [P3,P7,P12,P13] Yes: 4, No:0 [P4,P5,P6,P10,P14] Yes: 3, No:2 History noyes YES [P1,P2,P8] Yes: 0, No:3 [P9,P11] Yes: 2, No:0 Vision fairexcellent NOYES NO YES [P6,P14] Yes: 0, No:2 [P4,P5,P10] Yes: 3, No:0

44
44 CSE 300 Entropy and Information Gain S contains s i tuples of class C i for i = {1,..., m} Information measures info required to classify any arbitrary tuple Entropy of attribute A with values {a 1,a 2, …,a v } Information gained by branching on attribute A

45
45 CSE 300 Entropy and Information Gain Select attribute with the highest information gain (or greatest entropy reduction) Such attribute minimizes information needed to classify samples

46
46 CSE 300 Rule Induction IF conditions THEN Conclusion Eg: CN2 Concept description: Characterization: provides a concise and succinct summarization of given collection of data Comparison: provides descriptions comparing two or more collections of data Training set, testing set Imprecise Predictive Accuracy P/P+N

47
47 CSE 300 Example used in a Clinic Hip arthoplasty trauma surgeon predict patient’s long- term clinical status after surgery Outcome evaluated during follow-ups for 2 years 2 modeling techniques Naïve Bayesian classifier Decision trees Bayesian classifier P(outcome=good) = 0.55 (11/20 good) Probability gets updated as more attributes are considered P(timing=good|outcome=good) = 9/11 (0.846) P(outcome = bad) = 9/20 P(timing=good|outcome=bad) = 5/9

48
48 CSE 300 Nomogram

49
49 CSE 300 Bayesian Classification Bayesian classifier vs. decision tree Decision tree: predict the class label Bayesian classifier: statistical classifier; predict class membership probabilities Based on Bayes theorem; estimate posterior probability Na ï ve Bayesian classifier: Simple classifier that assumes attribute independence High speed when applied to large databases Comparable in performance to decision trees

50
50 CSE 300 Bayes Theorem Let X be a data sample whose class label is unknown Let H i be the hypothesis that X belongs to a particular class C i P(H i ) is class prior probability that X belongs to a particular class C i Can be estimated by n i /n from training data samples n is the total number of training data samples n i is the number of training data samples of class C i Formula of Bayes Theorem

51
51 CSE 300 More classification Techniques Neural Networks Similar to pattern recognition properties of biological systems Most frequently used Multi-layer perceptrons –Input with bias, connected by weights to hidden, output Backpropagation neural networks Support Vector Machines Separate database to mutually exclusive regions Transform to another problem space Kernel functions (dot product) Output of new points predicted by position Comparison with classification trees Not possible to know which features or combination of features most influence a prediction

52
52 CSE 300 Multilayer Perceptrons Non-linear transfer functions to weighted sums of inputs Werbos algorithm Random weights Training set, Testing set

53
53 CSE 300 Support Vector Machines 3 steps Support Vector creation Maximal distance between points found Perpendicular decision boundary Allows some points to be misclassified Pima Indian data with X1(glucose) X2(BMI)

54
54 CSE 300 What is Association Rule Mining? Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories Example of Association Rules {High LDL, Low HDL} {Heart Failure} PatientIDConditions 1 High LDL Low HDL, High BMI, Heart Failure 2 High LDL Low HDL, Heart Failure, Diabetes 3Diabetes 4 High LDL Low HDL, Heart Failure 5 High BMI, High LDL Low HDL, Heart Failure People who have high LDL (“bad” cholesterol), low HDL (“good cholesterol”) are at higher risk of heart failure.

55
55 CSE 300 Association Rule Mining Market Basket Analysis Same groups of items bought placed together Healthcare Understanding among association among patients with demands for similar treatments and services Goal : find items for which joint probability of occurrence is high Basket of binary valued variables Results form association rules, augmented with support and confidence

56
56 CSE 300 Association Rule Mining Association Rule An implication expression of the form X Y, where X and Y are itemsets and X Y= Rule Evaluation Metrics Support (s): Fraction of transactions that contain both X and Y Confidence (c): Measures how often items in Y appear in transactions that contain X Trans containing Y Trans containing both X and Y Trans containing X D

57
57 CSE 300 The Apriori Algorithm Starts with most frequent 1-itemset Include only those “items” that pass threshold Use 1-itemset to generate 2-itemsets Stop when threshold not satisfied by any itemset L 1 = {frequent items}; for (k = 1; L k != ; k++) do Candidate Generation: C k+1 = candidates generated from L k ; Candidate Counting: for each transaction t in database do increment the count of all candidates in C k+1 that are contained in t L k+1 = candidates in C k+1 with min_sup return k L k ;

58
58 CSE 300 Apriori-based Mining

59
59 CSE 300 Principle Component Analysis Principle Components In cases of large number of variables, highly possible that some subsets of the variables are very correlated with each other. Reduce variables but retain variability in dataset Linear combinations of variables in the database Variance of each PC maximized –Display as much spread of the original data PC orthogonal with each other –Minimize the overlap in the variables Each component normalized sum of square is unity –Easier for mathematical analysis Number of PC < Number of variables Associations found Small number of PC explain large amount of variance Example 768 female Pima Indians evaluated for diabetes Number of times pregnant, two-hour oral glucose tolerance test (OGTT) plasma glucose, Diastolic blood pressure, Triceps skin fold thickness, Two-hour serum insulin, BMI, Diabetes pedigree function, Age, Diabetes onset within last 5 years

60
60 CSE 300 PCA Example

61
61 CSE 300 National Cancer Institute CancerNet http://www.nci.nih.gov http://www.nci.nih.gov CancerNet for Patients and the Public CancerNet for Health Professionals CancerNet for Basic Reasearchers CancerLit

62
62 CSE 300Conclusion About ¾ billion of people’s medical records are electronically available Data mining in medicine distinct from other fields due to nature of data: heterogeneous, with ethical, legal and social constraints Most commonly used technique is classification and prediction with different techniques applied for different cases Associative rules describe the data in the database Medical data mining can be the most rewarding despite the difficulty

63
63 CSE 300 Thank you !!!

Similar presentations

Presentation is loading. Please wait....

OK

Data Mining Data Warehouses.

Data Mining Data Warehouses.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on job satisfaction of employees Ppt on 21st century skills for teachers Ppt on forward contracting Ppt on different occupations for kids Ppt on fire extinguisher types class Ppt on event driven programming examples Elaine marieb anatomy and physiology ppt on cells Free download ppt on india of my dreams Ppt on biotechnology in agriculture Ppt on hindu religion song