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a novel approach for breast cancer detection using data mining tool weka


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computer engineering
data mining tool
weka open source data mining tool
breast cancer classification
knn ,ibk ,svm

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a novel approach for breast cancer detection using data mining tool weka

  1. 1. A Novel Approach for Breast Cancer Detection using Data Mining Techniques Presented by: • Ahmed Abd Elhafeez • Ahmed Elbohy Under supervision of : Prof. Dr. Aliaa Youssif 13/27/2014 AAST-Comp eng
  2. 2. AGENDA  Scientific and Medical Background 1. What is cancer? 2. Breast cancer 3. History and Background 4. Pattern recognition system decomposition 5. About data mining 6. Data mining tools 7. Classification Techniques 2 3/27/2014AAST-Comp eng
  3. 3. AGENDA (Cont.)  Paper contents 1. Introduction 2. Related Work 3. Classification Techniques 4. Experiments and Results 5. Conclusion 6. References 3 3/27/2014AAST-Comp eng
  4. 4. What Is Cancer?  Cancer is a term used for diseases in which abnormal cells divide without control and are able to invade other tissues. Cancer cells can spread to other parts of the body through the blood and lymph systems.  Cancer is not just one disease but many diseases. There are more than 100 different types of cancer.  Most cancers are named for the organ or type of cell in which they start  There are two general types of cancer tumours namely: • benign • malignant 4 AAST-Comp eng 3/27/2014
  5. 5. Skin cancer Breast cancerColon cancer Lung cancer Pancreatic cancer Liver Bladder Prostate Cancer Kidney cancerThyroid Cancer Leukemia Cancer Edometrial Cancer Rectal Cancer Non-Hodgkin Lymphoma Cervical cancer Thyroid Cancer Oral cancer AAST-Comp eng 53/27/2014
  6. 6. Breast Cancer 6 • The second leading cause of death among women is breast cancer, as it comes directly after lung cancer. • Breast cancer considered the most common invasive cancer in women, with more than one million cases and nearly 600,000 deaths occurring worldwide annually. • Breast cancer comes in the top of cancer list in Egypt by 42 cases per 100 thousand of the population. However 80% of the cases of breast cancer in Egypt are of the benign kind. AAST-Comp eng3/27/2014
  7. 7. History and Background Medical Prognosis is the estimation of : • Cure • Complication • disease recurrence • Survival for a patient or group of patients after treatment. 7AAST-Comp eng3/27/2014
  8. 8. • There are a lot of works done for various diseases like cancer • like shown in paper [1].As technique used in it is very • convenient since the Decision Tree is simple to understand, • works with mixed data types, models non-linear functions, • handles classification, and most of the readily available tools • use it. Even in the paper [2] that I referred discusses how data • warehousing, data mining, and decision support systems can • reduce the national cancer burden or the oral complications of • cancer therapies. For this goal to be achieved, it first will be • necessary to monitor populations; collect relevant cancer • screening, incidence, treatment, and outcomes data; identify • cancer patterns; explain the patterns, and translate the • explanations into effective diagnoses and treatments. The next • paper that I referred [3] contains the evaluation of the breast • masses in a series of pathologically proven tumours using data • mining with decision tree model for classification of breast • tumours. Accuracy, sensitivity, specificity, positive predictive • value and negative predictive value are the five most generally • used objective indices to estimate the performance of diagnosis • results. Sensitivity and specificity are the most two important • indices that a doctor concerned about. With sensitivity 93.33% 3/27/2014 AAST-Comp eng 8
  9. 9. • 320 for the detection of bacteria causing eye infections using • pure laboratory cultures and the screening of bacteria • associated with ENT infections using actual hospital samples. • Bong-Horng chu and his team [5] propose a hybridized • architecture to deal with customer retention 3/27/2014 AAST-Comp eng 9
  10. 10. Breast Cancer Classification 10AAST-Comp eng Round well- defined, larger groups are more likely benign. Tight cluster of tiny, irregularly shaped groups may indicate cancer Malignant Suspicious pixels groups show up as white spots on a mammogram. 3/27/2014
  11. 11. Breast cancer’s Features • MRI - Cancer can have a unique appearance – features that turned out to be cancer used for diagnosis / prognosis of each cell nucleus. 11AAST-Comp eng F2Magnetic Resonance Image F1 F3 Fn Feature Extraction 3/27/2014
  12. 12. Diagnosis or prognosis Brest Cancer Benign Malignant AAST-Comp eng 123/27/2014
  13. 13. Computer-Aided Diagnosis • Mammography allows for efficient diagnosis of breast cancers at an earlier stage • Radiologists misdiagnose 10-30% of the malignant cases • Of the cases sent for surgical biopsy, only 10-20% are actually malignant 3/27/2014 AAST-Comp eng 13
  14. 14. Computational Intelligence Computational Intelligence Data + Knowledge Artificial Intelligence Expert systems Fuzzy logic Pattern Recognition Machine learning Probabilistic methods Multivariate statistics Visuali- zation Evolutionary algorithms Neural networks 3/27/2014 AAST-Comp eng 14
  15. 15. What do these methods do? • Provide non-parametric models of data. • Allow to classify new data to pre-defined categories, supporting diagnosis & prognosis. • Allow to discover new categories. • Allow to understand the data, creating fuzzy or crisp logical rules. • Help to visualize multi-dimensional relationships among data samples.3/27/2014 AAST-Comp eng 15
  16. 16. Feature selection Data Preprocessing Selecting Data mining tooldataset Classification algorithm SMO IBK BF TREE Results and evaluations AAST-Comp eng Pattern recognition system decomposition 3/27/2014
  17. 17. Results Data preprocessing Feature selectionClassification Selection tool data mining Performance evaluation Cycle Dataset
  18. 18. data sets AAST-Comp eng 183/27/2014
  19. 19. results Data preprocessing Feature selectionclassification Selection tool datamining Performance evaluation Cycle Dataset
  20. 20. AAST-Comp eng 20 Data Mining • Data Mining is set of techniques used in various domains to give meaning to the available data • Objective: Fit data to a model –Descriptive –Predictive 3/27/2014
  21. 21. Predictive & descriptive data mining • Predictive: Is the process of automatically creating a classification model from a set of examples, called the training set, which belongs to a set of classes. Once a model is created, it can be used to automatically predict the class of other unclassified examples • Descriptive : Is to describe the general or special features of a set of data in a concise manner AAST-Comp eng 213/27/2014
  22. 22. AAST-Comp eng 22 Data Mining Models and Tasks 3/27/2014
  23. 23. Data mining Tools Many advanced tools for data mining are available either as open-source or commercial software. 23AAST-Comp eng3/27/2014
  24. 24. weka • Waikato environment for knowledge analysis • Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. • Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. • Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. 3/27/2014 AAST-Comp eng 24
  25. 25. Results Data preprocessing Feature selection Classification Selection tool data mining Performance evaluation Cycle Dataset
  26. 26. Data Preprocessing • Data in the real world is : – 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 • Quality decisions must be based on quality data measures: Accuracy ,Completeness, Consistency, Timeliness, Believability, Value added and Accessibility AAST-Comp eng 263/27/2014
  27. 27. Preprocessing techniques • Data cleaning – Fill in missing values, smooth noisy data, identify or remove outliers and resolve inconsistencies • Data integration – Integration of multiple databases, data cubes or files • Data transformation – Normalization and aggregation • Data reduction – Obtains reduced representation in volume but produces the same or similar analytical results • Data discretization – Part of data reduction but with particular importance, especially for numerical data AAST-Comp eng 273/27/2014
  28. 28. Results Data preprocessing Feature selection Classification Selection tool datamining Performance evaluation Cycle Dataset
  29. 29. Finding a feature subset that has the most discriminative information from the original feature space. The objective of feature selection is : • Improving the prediction performance of the predictors • Providing a faster and more cost-effective predictors • Providing a better understanding of the underlying process that generated the data Feature selection AAST-Comp eng 293/27/2014
  30. 30. Feature Selection • Transforming a dataset by removing some of its columns A1 A2 A3 A4 C A2 A4 C 3/27/2014 AAST-Comp eng 30
  31. 31. Results Data preprocessing Feature selection Classification Selection tool data mining Performance evaluation Cycle Dataset
  32. 32. Supervised Learning • Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations • New data is classified based on the model built on training set known categories AAST-Comp eng Category ―A‖ Category ―B‖ Classification (Recognition) (Supervised Classification) 323/27/2014
  33. 33. Classification • Everyday, all the time we classify things. • Eg crossing the street: – Is there a car coming? – At what speed? – How far is it to the other side? – Classification: Safe to walk or not!!! 3/27/2014 AAST-Comp eng 33
  34. 34. 3/27/2014 AAST-Comp eng 34  Classification:  predicts categorical class labels (discrete or nominal)  classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data  Prediction:  models continuous-valued functions, i.e., predicts unknown or missing values Classification vs. Prediction
  35. 35. 3/27/2014 AAST-Comp eng 35 Classification—A Two-Step Process  Model construction: describing a set of predetermined classes  Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute  The set of tuples used for model construction is training set  The model is represented as classification rules, decision trees, or mathematical formulae  Model usage: for classifying future or unknown objects  Estimate accuracy of the model  The known label of test sample is compared with the classified result from the model  Accuracy rate is the percentage of test set samples that are correctly classified by the model  Test set is independent of training set, otherwise over-fitting will occur  If the accuracy is acceptable, use the model to classify data tuples whose class labels are not known
  36. 36. 3/27/2014 AAST-Comp eng 36 Classification Process (1): Model Construction Training Data NAME RANK YEARS TENURED Mike Assistant Prof 3 no Mary Assistant Prof 7 yes Bill Professor 2 yes Jim Associate Prof 7 yes Dave Assistant Prof 6 no Anne Associate Prof 3 no Classification Algorithms IF rank = „professor‟ OR years > 6 THEN tenured = „yes‟ Classifier (Model)
  37. 37. 3/27/2014 AAST-Comp eng 37 Classification Process (2): Use the Model in Prediction Classifier Testing Data NAME RANK YEARS TENURED Tom Assistant Prof 2 no Merlisa Associate Prof 7 no George Professor 5 yes Joseph Assistant Prof 7 yes Unseen Data (Jeff, Professor, 4) Tenured?
  38. 38. Classification • is a data mining (machine learning) technique used to predict group membership for data instances. • Classification analysis is the organization of data in given class. • These approaches normally use a training set where all objects are already associated with known class labels. • The classification algorithm learns from the training set and builds a model. • Many classification models are used to classify new objects. AAST-Comp eng 383/27/2014
  39. 39. Classification • predicts categorical class labels (discrete or nominal) • constructs a model based on the training set and the values (class labels) in a classifying attribute and uses it in classifying unseen data AAST-Comp eng 393/27/2014
  40. 40. Quality of a classifier • Quality will be calculated with respect to lowest computing time. • Quality of certain model one can describe by confusion matrix. • Confusion matrix shows a new entry properties predictive ability of the method. • Row of the matrix represents the instances in a predicted class, while each column represents the instances in an actual class. • Thus the diagonal elements represent correctly classified compounds • the cross-diagonal elements represent misclassified compounds. AAST-Comp eng 403/27/2014
  41. 41. Classification Techniques  Building accurate and efficient classifiers for large databases is one of the essential tasks of data mining and machine learning research  The ultimate reason for doing classification is to increase understanding of the domain or to improve predictions compared to unclassified data. 3/27/2014AAST-Comp eng41
  42. 42. Classification Techniques classificatio n Techniques Naïve Bays SVM C4.5 KNN BF tree IBK 42 3/27/2014AAST-Comp eng
  43. 43. Classification Model Support vector machine Classifier V. Vapnik 3/27/2014 AAST-Comp eng 43
  44. 44. Support Vector Machine (SVM)  SVM is a state-of-the-art learning machine which has been extensively used as a tool for data classification , function approximation, etc.  due to its generalization ability and has found a great deal of success in many applications.  Unlike traditional methods which minimizing the empirical training error, a noteworthy feature of SVM is that it minimize an upper bound of the generalization error through maximizing the margin between the separating hyper-plane and a data set 3/27/2014AAST-Comp eng44
  45. 45. Support Vector Machine (SVM) 3/27/2014AAST-Comp eng45  SVM is a state-of-the-art learning machine which has been extensively used as a tool for data classification , function approximation, etc.  due to its generalization ability and has found a great deal of success in many applications.  Unlike traditional methods which minimizing the empirical training error, a noteworthy feature of SVM is that it minimize an upper bound of the generalization error through maximizing the margin between the separating hyper-plane and a data set
  46. 46. Tennis example Humidity Temperature = play tennis = do not play tennis 3/27/2014 AAST-Comp eng 46
  47. 47. Linear classifiers: Which Hyperplane? • Lots of possible solutions for a, b, c. • Some methods find a separating hyperplane, but not the optimal one • Support Vector Machine (SVM) finds an optimal solution. – Maximizes the distance between the hyperplane and the “difficult points” close to decision boundary – One intuition: if there are no points near the decision surface, then there are no very uncertain classification decisions 47 This line represents the decision boundary: ax + by − c = 0 Ch. 15 3/27/2014 AAST-Comp eng
  48. 48. Selection of a Good Hyper-Plane Objective: Select a `good' hyper-plane using only the data! Intuition: (Vapnik 1965) - assuming linear separability (i) Separate the data (ii) Place hyper-plane `far' from data 3/27/2014 AAST-Comp eng 48
  49. 49. SVM – Support Vector Machines Support Vectors Small Margin Large Margin 3/27/2014 AAST-Comp eng 49
  50. 50. Support Vector Machine (SVM) • SVMs maximize the margin around the separating hyperplane. • The decision function is fully specified by a subset of training samples, the support vectors. • Solving SVMs is a quadratic programming problem • Seen by many as the most successful current text classification method 50 Support vectors Maximizes margin Sec. 15.1 Narrower margin 3/27/2014 AAST-Comp eng
  51. 51. Non-Separable Case 3/27/2014 AAST-Comp eng 51 The Lagrangian trick
  52. 52. SVM  SVM  Relatively new concept  Nice Generalization properties  Hard to learn – learned in batch mode using quadratic programming techniques  Using kernels can learn very complex functions 3/27/2014 AAST-Comp eng 53
  53. 53. Classification Model K-Nearest Neighbor Classifier3/27/2014 AAST-Comp eng 54
  54. 54. K-Nearest Neighbor Classifier Learning by analogy: Tell me who your friends are and I’ll tell you who you are A new example is assigned to the most common class among the (K) examples that are most similar to it. 3/27/2014 AAST-Comp eng 55
  55. 55. K-Nearest Neighbor Algorithm  To determine the class of a new example E:  Calculate the distance between E and all examples in the training set  Select K-nearest examples to E in the training set  Assign E to the most common class among its K- nearest neighbors Response Response No response No response No response Class: Response 3/27/2014 AAST-Comp eng 56
  56. 56.  Each example is represented with a set of numerical attributes  ―Closeness‖ is defined in terms of the Euclidean distance between two examples.  The Euclidean distance between X=(x1, x2, x3,…xn) and Y =(y1,y2, y3,…yn) is defined as:  Distance (John, Rachel)=sqrt [(35-41)2+(95K-215K)2 +(3-2)2] n i ii yxYXD 1 2 )(),( John: Age=35 Income=95K No. of credit cards=3 Rachel: Age=41 Income=215K No. of credit cards=2 Distance Between Neighbors 3/27/2014 AAST-Comp eng 57
  57. 57. Instance Based Learning  No model is built: Store all training examples  Any processing is delayed until a new instance must be classified. Response Response No response No response No response Class: Respond 3/27/2014 AAST-Comp eng 58
  58. 58. Example : 3-Nearest Neighbors Customer Age Income No. credit cards Response John 35 35K 3 No Rachel 22 50K 2 Yes Hannah 63 200K 1 No Tom 59 170K 1 No Nellie 25 40K 4 Yes David 37 50K 2 ? 3/27/2014 AAST-Comp eng 59
  59. 59. Customer Age Income (K) No. cards John 35 35 3 Rachel 22 50 2 Hannah 63 200 1 Tom 59 170 1 Nellie 25 40 4 David 37 50 2 Response No Yes No No Yes Distance from David sqrt [(35-37)2+(35-50)2 +(3-2)2]=15.16 sqrt [(22-37)2+(50-50)2 +(2-2)2]=15 sqrt [(63-37)2+(200- 50)2 +(1-2)2]=152.23 sqrt [(59-37)2+(170- 50)2 +(1-2)2]=122 sqrt [(25-37)2+(40-50)2 +(4-2)2]=15.74 Yes 3/27/2014 AAST-Comp eng 60
  60. 60. Strengths and Weaknesses Strengths:  Simple to implement and use  Comprehensible – easy to explain prediction  Robust to noisy data by averaging k-nearest neighbors. Weaknesses:  Need a lot of space to store all examples.  Takes more time to classify a new example than with a model (need to calculate and compare distance from new example to all other examples). 3/27/2014 AAST-Comp eng 61
  61. 61. Decision Tree 3/27/2014 AAST-Comp eng 62
  62. 62. – Decision tree induction is a simple but powerful learning paradigm. In this method a set of training examples is broken down into smaller and smaller subsets while at the same time an associated decision tree get incrementally developed. At the end of the learning process, a decision tree covering the training set is returned. – The decision tree can be thought of as a set sentences written propositional logic. 3/27/2014 AAST-Comp eng 63
  63. 63. Example Jenny Lind is a writer of romance novels. A movie company and a TV network both want exclusive rights to one of her more popular works. If she signs with the network, she will receive a single lump sum, but if she signs with the movie company, the amount she will receive depends on the market response to her movie. What should she do? 3/27/2014 AAST-Comp eng 64
  64. 64. Payouts and Probabilities • Movie company Payouts – Small box office - $200,000 – Medium box office - $1,000,000 – Large box office - $3,000,000 • TV Network Payout – Flat rate - $900,000 • Probabilities – P(Small Box Office) = 0.3 – P(Medium Box Office) = 0.6 – P(Large Box Office) = 0.1 3/27/2014 AAST-Comp eng 65
  65. 65. Jenny Lind - Payoff Table Decisions States of Nature Small Box Office Medium Box Office Large Box Office Sign with Movie Company $200,000 $1,000,000 $3,000,000 Sign with TV Network $900,000 $900,000 $900,000 Prior Probabilities 0.3 0.6 0.1 3/27/2014 AAST-Comp eng 66
  66. 66. Using Expected Return Criteria EVmovie=0.3(200,000)+0.6(1,000,000)+0.1(3,000,000) = $960,000 = EVUII or EVBest EVtv =0.3(900,000)+0.6(900,000)+0.1(900,000) = $900,000 Therefore, using this criteria, Jenny should select the movie contract. 3/27/2014 AAST-Comp eng 67
  67. 67. Decision Trees • Three types of “nodes” – Decision nodes - represented by squares ( ) – Chance nodes - represented by circles (Ο) – Terminal nodes - represented by triangles (optional) • Solving the tree involves pruning all but the best decisions at decision nodes, and finding expected values of all possible states of nature at chance nodes • Create the tree from left to right • Solve the tree from right to left 3/27/2014 AAST-Comp eng 68
  68. 68. Example Decision Tree Decision node Chance node Event 1 Event 2 Event 3 3/27/2014 AAST-Comp eng 69
  69. 69. Jenny Lind Decision Tree Small Box Office Medium Box Office Large Box Office Small Box Office Medium Box Office Large Box Office Sign with Movie Co. Sign with TV Network $200,000 $1,000,000 $3,000,000 $900,000 $900,000 $900,000 3/27/2014 AAST-Comp eng 70
  70. 70. Jenny Lind Decision Tree Small Box Office Medium Box Office Large Box Office Small Box Office Medium Box Office Large Box Office Sign with Movie Co. Sign with TV Network $200,000 $1,000,000 $3,000,000 $900,000 $900,000 $900,000 .3 .6 .1 .3 .6 .1 ER ? ER ? ER ? 3/27/2014 AAST-Comp eng 71
  71. 71. Jenny Lind Decision Tree - Solved Small Box Office Medium Box Office Large Box Office Small Box Office Medium Box Office Large Box Office Sign with Movie Co. Sign with TV Network $200,000 $1,000,000 $3,000,000 $900,000 $900,000 $900,000 .3 .6 .1 .3 .6 .1 ER 900,000 ER 960,000 ER 960,000 3/27/2014 AAST-Comp eng 72
  72. 72. Results Data preprocessing Feature selection Classification Selection tool data mining Performance evaluation Cycle Dataset
  73. 73. Evaluation Metrics Predicted as healthy Predicted as unhealthy Actual healthy tp fn Actual not healthy fp tn AAST-Comp eng 743/27/2014
  74. 74. Cross-validation • Correctly Classified Instances 143 95.3% • Incorrectly Classified Instances 7 4.67 % • Default 10-fold cross validation i.e. – Split data into 10 equal sized pieces – Train on 9 pieces and test on remainder – Do for all possibilities and average 3/27/2014 AAST-Comp eng 75
  75. 75. A Novel Approach for Breast Cancer Detection using Data Mining Techniques 76 3/27/2014AAST-Comp eng
  76. 76. Abstract  The aim of this paper is to investigate the performance of different classification techniques.  Aim is developing accurate prediction models for breast cancer using data mining techniques  Comparing three classification techniques in Weka software and comparison results.  Sequential Minimal Optimization (SMO) has higher prediction accuracy than IBK and BF Tree methods. 77 3/27/2014AAST-Comp eng
  77. 77. Introduction  Breast cancer is on the rise across developing nations  due to the increase in life expectancy and lifestyle changes such as women having fewer children.  Benign tumors: • Are usually not harmful • Rarely invade the tissues around them • Don‘t spread to other parts of the body • Can be removed and usually don‘t grow back  Malignant tumors: • May be a threat to life • Can invade nearby organs and tissues (such as the chest wall) • Can spread to other parts of the body • Often can be removed but sometimes grow back 78 3/27/2014AAST-Comp eng
  78. 78. Risk factors  Gender  Age  Genetic risk factors  Family history  Personal history of breast cancer  Race : white or black  Dense breast tissue :denser breast tissue have a higher risk  Certain benign (not cancer) breast problems  Lobular carcinoma in situ  Menstrual periods 79 3/27/2014AAST-Comp eng
  79. 79. Risk factors  Breast radiation early in life  Treatment with DES : the drug DES (diethylstilbestrol) during pregnancy  Not having children or having them later in life  Certain kinds of birth control  Using hormone therapy after menopause  Not breastfeeding  Alcohol  Being overweight or obese 80 3/27/2014AAST-Comp eng
  80. 80. BACKGROUND  Bittern et al. used artificial neural network to predict the survivability for breast cancer patients. They tested their approach on a limited data set, but their results show a good agreement with actual survival Traditional segmentation  Vikas Chaurasia et al. used Representive Tree, RBF Network and Simple Logistic to predict the survivability for breast cancer patients.  Liu Ya-Qin‘s experimented on breast cancer data using C5 algorithm with bagging to predict breast cancer survivability. 81 3/27/2014AAST-Comp eng
  81. 81. BACKGROUND  Bellaachi et al. used naive bayes, decision tree and back-propagation neural network to predict the survivability in breast cancer patients. Although they reached good results (about 90% accuracy), their results were not significant due to the fact that they divided the data set to two groups; one for the patients who survived more than 5 years and the other for those patients who died before 5 years.  Vikas Chaurasia et al. used Naive Bayes, J48 Decision Tree to predict the survivability for Heart Diseases patients. 82 3/27/2014AAST-Comp eng
  82. 82. BACKGROUND  Vikas Chaurasia et al. used CART (Classification and Regression Tree), ID3 (Iterative Dichotomized 3) and decision table (DT) to predict the survivability for Heart Diseases patients.  Pan wen conducted experiments on ECG data to identify abnormal high frequency electrocardiograph using decision tree algorithm C4.5.  Dong-Sheng Cao‘s proposed a new decision tree based ensemble method combined with feature selection method backward elimination strategy to find the structure activity relationships in the area of chemo metrics related to pharmaceutical industry.83 3/27/2014AAST-Comp eng
  83. 83. BACKGROUND  Dr. S.Vijayarani et al., analyses the performance of different classification function techniques in data mining for predicting the heart disease from the heart disease dataset. The classification function algorithms is used and tested in this work. The performance factors used for analyzing the efficiency of algorithms are clustering accuracy and error rate. The result illustrates shows logistics classification function efficiency is better than multilayer perception and sequential minimal optimization.84 3/27/2014AAST-Comp eng
  84. 84. BACKGROUND  Kaewchinporn C‘s presented a new classification algorithm TBWC combination of decision tree with bagging and clustering. This algorithm is experimented on two medical datasets: cardiocography1, cardiocography2 and other datasets not related to medical domain.  BS Harish et al., presented various text representation schemes and compared different classifiers used to classify text documents to the predefined classes. The existing methods are compared and contrasted based on various parameters85 3/27/2014AAST-Comp eng
  86. 86. BREAST-CANCER-WISCONSIN DATA SET SUMMARY  the UC Irvine machine learning repository  Data from University of Wisconsin Hospital, Madison, collected by dr. W.H. Wolberg.  2 classes (malignant and benign), and 9 integer- valued attributes  breast-cancer-Wisconsin having 699 instances  We removed the 16 instances with missing values from the dataset to construct a new dataset with 683 instances  Class distribution: Benign: 458 (65.5%) Malignant: 241 (34.5%)  Note :2 malignant and 14 benign excluded hence percentage is wrong and the right one is :  benign 444 (65%) and malignant 239 (35%) 3/27/2014AAST-Comp eng87
  87. 87. 3/27/2014 AAST-Comp eng 88 Attribute Domain Sample Code Number Id Number Clump Thickness 1 - 10 Uniformity Of Cell Size 1 - 10 Uniformity Of Cell Shape 1 - 10 Marginal Adhesion 1 - 10 Single Epithelial Cell Size 1 - 10 Bare Nuclei 1 - 10 Bland Chromatin 1 - 10 Normal Nucleoli 1 - 10 Mitoses 1 - 10 Class 2 For Benign 4 For Malignant
  88. 88. EVALUATION METHODS  We have used the Weka (Waikato Environment for Knowledge Analysis). version 3.6.9  WEKA is a collection of machine learning algorithms for data mining tasks.  The algorithms can either be applied directly to a dataset or called from your own Java code.  WEKA contains tools for data preprocessing, classification, regression, clustering, association rules, visualization and feature selection.  It is also well suited for developing new machine learning schemes.  WEKA is open source software issued under the GNU General Public License 3/27/2014AAST-Comp eng89
  89. 89. EXPERIMENTAL RESULTS 90 3/27/2014AAST-Comp eng
  90. 90. EXPERIMENTAL RESULTS 91 3/27/2014AAST-Comp eng
  91. 91. importance of the input variables 3/27/2014AAST-Comp eng92 Domain 1 2 3 4 5 6 7 8 9 10 Sum Clump Thickness 139 50 104 79 128 33 23 44 14 69 683 Uniformity of Cell Size 373 45 52 38 30 25 19 28 6 67 683 Uniformity of Cell Shape 346 58 53 43 32 29 30 27 7 58 683 Marginal Adhesion 393 58 58 33 23 21 13 25 4 55 683 Single Epithelial Cell Size 44 376 71 48 39 40 11 21 2 31 683 Bare Nuclei 402 30 28 19 30 4 8 21 9 132 683 Bare Nuclei 150 160 161 39 34 9 71 28 11 20 683 Normal Nucleoli 432 36 42 18 19 22 16 23 15 60 683 Mitoses 563 35 33 12 6 3 9 8 0 14 683 Sum 2843 850 605 333 346 192 207 233 77 516
  92. 92. EXPERIMENTAL RESULTS 93 3/27/2014AAST-Comp eng Evaluation Criteria Classifiers BF TREE IBK SMO Timing To Build Model (In Sec) 0.97 0.02 0.33 Correctly Classified Instances 652 655 657 Incorrectly Classified Instances 31 28 26 Accuracy (%) 95.46% 95.90% 96.19%
  93. 93. EXPERIMENTAL RESULTS  The sensitivity or the true positive rate (TPR) is defined by TP / (TP + FN)  the specificity or the true negative rate (TNR) is defined by TN / (TN + FP)  the accuracy is defined by (TP + TN) / (TP + FP + TN + FN).  True positive (TP) = number of positive samples correctly predicted.  False negative (FN) = number of positive samples wrongly predicted.  False positive (FP) = number of negative samples wrongly predicted as positive.  True negative (TN) = number of negative samples correctly predicted 94 3/27/2014AAST-Comp eng
  94. 94. EXPERIMENTAL RESULTS Classifier TP FP Precision Recall Class BF Tree 0.971 0.075 0.96 0.971 Benign 0.925 0.029 0.944 0.925 Malignant IBK 0.98 0.079 0.958 0.98 Benign 0.921 0.02 0.961 0.921 Malignant SMO 0.971 0.054 0.971 0.971 Benign 0.946 0.029 0.946 0.946 Malignant 95 3/27/2014AAST-Comp eng
  95. 95. EXPERIMENTAL RESULTS Classifier Benign Malignant Class BF Tree 431 13 Benign 18 221 Malignant IBK 435 9 Benign 19 220 Malignant SMO 431 13 Benign 13 226 Malignant 96 3/27/2014AAST-Comp eng
  96. 96. importance of the input variables 3/27/2014AAST-Comp eng97 variable Chi- squared Info Gain Gain Ratio Average Rank IMPORTANCE Clump Thickness 378.08158 0.464 0.152 126.232526 8 Uniformity of Cell Size 539.79308 0.702 0.3 180.265026 1 Uniformity of Cell Shape 523.07097 0.677 0.272 174.673323 2 Marginal Adhesion 390.0595 0.464 0.21 130.2445 7 Single Epithelial Cell Size 447.86118 0.534 0.233 149.542726 5 Bare Nuclei 489.00953 0.603 0.303 163.305176 3 Bland Chromatin 453.20971 0.555 0.201 151.321903 4 Normal Nucleoli 416.63061 0.487 0.237 139.118203 6 Mitoses 191.9682 0.212 0.212 64.122733 9
  97. 97. 3/27/2014AAST-Comp eng98
  98. 98. CONCLUSION.  the accuracy of classification techniques is evaluated based on the selected classifier algorithm.  we used three popular data mining methods: Sequential Minimal Optimization (SMO), IBK, BF Tree.  The performance of SMO shows the high level compare with other classifiers.  most important attributes for breast cancer survivals are Uniformity of Cell Size. 99 3/27/2014AAST-Comp eng
  99. 99. Future work  using updated version of weka  Using another data mining tool  Using alternative algorithms and techniques 3/27/2014AAST-Comp eng100
  100. 100. Notes on paper  Spelling mistakes  No point of contact (e - mail)  Wrong percentage calculation  Copying from old papers  Charts not clear  No contributions 3/27/2014AAST-Comp eng101
  101. 101. comparison  Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers written  International Journal of Computer and Information Technology (2277 – 0764) Volume 01– Issue 01, September 2012  Paper introduced more advanced idea and make a fusion between classifiers 3/27/2014AAST-Comp eng102
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