Predicting Breast Cancer Survivability Rates

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1 Predicting Breast Cancer Survivability Rates For data collected from Saudi Arabia Registries Ghofran Othoum 1 and Wadee Al-Halabi 2 1 Computer Science, Effat University, Jeddah, Saudi Arabia 2 Computer Science, Effat University, Jeddah, Saudi Arabia Abstract The application of data mining and machine learning in directing clinical research into possible hidden knowledge is becoming greatly influencial in cancer research. This research presents a comparison of three data mining classification models: multi-layer perceptron neural networks, C4.5 decision trees and Naive Bayes. The classification models are built for breast cancer survivability prediction. The data set used is collected from registries across Saudi Arabia. Due to data scarcity, data synthesis had to be performed using a random seed and a double sampling procedure. After sufficiently preprocessing the data, the classification models were built and three performance measures were used to rank the models: Accuracy, Sensitivity and Specificity. The experiment was set up with multi-layer perceptron as the baseline scheme and with statistical significance of Decision Trees performed marginally better than multi-layer perceptron and Naïve Bayes performed significantly worse than the baseline scheme. The result showed that Decsion tree is the the most accurate predictor for breast cancer survivbility in Saudi Arabia (Accuracy 0.979% ). Keywords: Data Mining, Breast Cancer Prediction, Neural Networks, Decision Trees, Naïve Bayes. 1 INTRODUCTION With unprecedented growth of data, especially in bioinformatics and the medical field, it has become very important to device computer-based methodologies to analyze meaningful and biologically significant information explosive data banks (Knowledge discovery). Data mining is an algorithmic technique used to describe relationships between patterns and predicting classifications based on the data. Cancer is a malignant disease that is one of the leading causes of deaths worldwide. However, survivability rates in cancer are systematically affected by different interdependent factors. These factors include: genetic factors; genes expressivity which determines phonotypical conditions (including cancer), lifestyle factors and medical history. These factors can be obtained through different data sets. This research aims at implementing data mining algorithms, applying them to data banks collected from various hospitals in Saudi Arabia, and then evaluating the classification models built. The three prediction models are: neural networks, decision trees and Naïve Bayes. 2 SIMILAR WORK Till today, there are numerous studies and projects that explore the benefits of using machine learning methods to predict cancer survivability. However, it was noticeable that there are few cancer classification projects completed in Saudi Arabia. The available global researches were investigated in order to formulate a generic structure of how the research can be conducted. Bellaachia and Guven s [1] used statistical data provided by the National Cancer Institute in the United States through SEER (Surveillance Epidemiology and End Results). The data set contained 16 fields in a total of 151,886 records. In their methodology, the authors investigated three data mining algorithms (Naïve Bayes, back-propagated neural network, and C4.5 decision tree algorithm) to predict survivability rates of cancer patients. WEKA (Weikota Environment of Knowledge Analysis) was used to implement the algorithms. K-fold cross validation was used to validate the results. Their hypothesis implies that their results differ from Delen s and Walker s [2]. The hypothesis was verified as the results showed that the methodology that Bellaachia and Guven used outperforms Delen et al approach. Scharber [3] investigated the role of text data mining in predicting cancer survivability. The result of this paper included that using text data mining helps in identifying vital cancer information like cancer type, tumor size and medical history information. These pieces of information, according to Scharber, will improve the treatment plan for each cancer patient. In Zhou,Z & Jiang,Y[4] study, case studies on diabetes, hepatitis, and breast cancer were used in this research paper as the dataset. Neural network ensemble, a

2 collection of artificial neural networks, was compared to C4.5 Rule-PANE. Neural Network was used as a preprocessor tool and C4.5 Rule-PANE as the main training algorithm. The results showed that C4.5 Rule-PANE is a powerful rule generator. The rules generated by C4.5 have strong generalization ability. Shital Shah, Andrew Kusiak[5] analyzed gene expression data to identify and classify cancer based on its causing genes. For prediction generator, decision tree and support vector machines algorithms were used. The data bank used contained datasets for ovarian, prostate, and lung cancer. This study, integrated all of the algorithms specified in a single gene-finding algorithm. For each type of cancer studied, a set of the most significant gene set was identified with accuracy range of 94-98%. 3 DATA CLEANING AND PREPARATION Before the dataset is used, it needs to be properly preprocessed and a complete relevancy analysis needs to be completed. Preprocessing entails functions like replacing missing values, normalizing numeric attributes and converting discrete attributes to nominal type. Feature selection involves selecting the attributes that are most relevant to the classification problem. The method used in relevancy analysis is information gain ranker. Below is a detailed presentations of the steps completed in the preprocessing and relevancy analysis (Feature Selection) phases. 3.1 Data Preprocessing WEKA s filters for feature selection are an integral component in the WEKA package. These filters can either be supervised, belonging to weka.filters.supervised, or unsupervised, belonging to weka.filters.unsupervised. From the unsupervised filters, numeric to nominal conversion was used for nominal attributes and normalization for numerical attributes. Attributes that had more than 70.0% missing values, were omitted. However, some fields contributions to the pattern were critical and it was considered inefficient to remove them. Table 1 shows the distribution of the class attribute over the collected data from various registries. 1.1 Feature selection In order to avoid inaccurate or random predictions including redundant, insignificant or noisy attributes, features were selected to be included in the classification (See table 2). This vector of attributes was sent to the Ministry of Health and various hospitals in order to get similar data fields from Saudi Hospital Registries. Table1 Survivability Distribution in Data Collected from Registries in Saudi Arabia (after random sampling) Categorical Variable Frequency Percentage Survive Breast Cancer % Not survive breast Cancer % Total 1358 Table2 Predictor Variables Attribute Description Marital status This data item identifies the patient s marital status at the time of diagnosis for the reportable tumor. Birth Place Patient s birth place Laterality The side of the organ in which tumor was detected Age at diagnosis Grade Differentiation of cells Radiation The radiation therapy methodology Survivability 1 for not survive and 0 for survive Sex Primary Site The origination site of the tumor 2 PREDICTION MODEL ANALYSIS 2.1 C4.5 J48 in WEKA refers to Quinlan s C4.5 algorithm with optional pruning. J48 is used to build decision trees from a set of labeled training data using the concept of information entropy. To split the data at each stage of the tree construction, a test is performed to select an attribute with the lowest entropy. Information gain (IG) (as shown in equation 3) is used as a measure of entropy (H) with respect to the class attribute (C) : H(C) = -Σp(c) logp(c), c Є C (1) H (C Xi) = -Σp(x) [ Σp(c x)log p(c x) ] (2) IGi = H(C) - H (C Xi) (3) [1] In survivability analysis, C would be the survivability class. The choice of the attribute in which the branch is formed depends on a low entropy value and high information gain value. In each iteration, if an entropy value is detected to be higher than in the previous iteration, the tree is pruned. Tree pruning requires the removal of the branch with high entropy value. CS4.5 accurate performance is attributed to its ability to split continuous attributes [7]. Each leaf in a decision tree constructed using C 4.5 is a rule. 2.2 Naïve Bayes The naive Bayes model is a classical data mining algorithm. It is commonly used to solve prediction problems for its ease of implementation and usage. At the same time, its simplicity doesn t undermine its robustness and effectiveness. Text classification is one of its most common implementations. Throughout the years, it has been through various improvements that are not only reflected in its data

3 mining capabilities but also in its machine learning pattern recognition. 2.3 Multi-layer perceptron neural network To solve this non linear classification problem, a multi-layer perceptron with back propagation learning was employed for structuring the model. The network was divided into input, hidden and output layers. In the input layer, the number of neurons was specified by the attributes and the number of output neurons where implied by the possible values of class attributes in this classification. The initial weights were assigned randomly for the connections of the networks and sigmoid function was used as the activation function in order to process the input at each layer and pass it to the next layer using the following equation: (2) Where v is the weighted sum of the input nodes. Five main steps are used in a back propagation- neural network learning phase. These steps are completed iteratively until the error propagated is small enough. 1. Randomly assigning weights to the network 2. Feed forward computation of the activation function 3. Back propagation of the error function to the output layer 4. Back propagation of the error function to the hidden layers 5. Change weights accordingly 3 CLASSIFIER S EVALUATION The experimenter module in WEKA shows both text and graphical representation of the results. However, each classifier can have its own additional graphical representations such as decision trees in C4.5. Moreover, the performance parameters that are common amongst all models are: the number of instances that are correctly classified, the number of instances that are incorrectly classified, kappa statistic which measures the agreement of the prediction with the actual classes. Also, there are error Figure 1 Neural Network Architecture measurements such as root mean squared error, mean absolute error, relative absolute error, and root relative squared error. 3.1 Confusion Matrix confusion matrix is a measure used to tabulate the results of a classifier as true positive, true negative, false positive and false negative. The confusion matrix is built to interpret the results of the classifier. The upper row in a confusion matrix represents the number of instances classified for the positive class and the lower row for the negative class. The true positive cell identifies the attributes that are correctly classified for the positive class where the false positive identifies the classes that are incorrectly classified for the positive class, whilst true negative and false negative represents the attributes that are correctly and incorrectly classified for the negative class respectively. (See table 3) 3.2 KAPPA Statistics Kappa statistics are used to indicate the correlation between the predicted values and the actual values. These parameters measure the pair-wise agreement between different observed values. [6] Based on the values in the confusion matrix, a kappa value of 1 indicates complete agreement; a kappa value between 0.61 and 0.80 indicates significant agreement. The built classifiers are expected to have a kappa parameter value of more than zero indicating that the predicted classification is not completed by random chance. 3.3 Performance Measures Three main performance measures are used: accuracy, sensitivity and specificity using the following equations respectively: 1. Accuracy= 2. Sensitivity = 3. Specificity = Where TP is true positive, TN true negative, FP false positive and FN false negative. These three measures will be used for this binary classification problem as follows: Sensitivity will indicate the ratio of how many cases were truly classified as survived out of all which have not been truly classified not survived (true positive and false negative). Specificity will indicate the ratio of how many cases were truly classified as not survived out of those who have not been classified as survived (True negative and false positive). Accuracy will indicate the ratio of truly classified instances out of all instances (true positive, true negative, (6) (7) (5)

4 false positive and false negative). Sensitivity is referred to as the true positive rate (TPR) and specificity the true negative rate (TNR). Thus, the sum of the TPR and the TNR should equal 1. The receiver operator characteristics graph is highly used in diagnosis in the medical field as ROC analysis. It compares the quality of different parameters by plotting the TP rate or sensitivity as the independent variable and the FP rate or specificity as the dependant variable. The classifier quality will be assessed based on the area under the graph. As the area under a ROC graph increases for a classifier, the quality of the classifier increases as well. 4 RESULTS After the three Models were built using 80% split and evaluated using 10 fold cross validation. Decision trees had the highest accuracy and sensitivity with and respectively. Neural Networks had the highest specificity with The table below shows the detailed performance of the three models. The ROC Area was lowest for naïve bayes with an area of 0.873(see Figure1) and highest for decision trees with ROC (Receiver Operating Curve) area of (see Figure 2). Multi-layer perceptron had a ROC area of (see Figure 3). Table3. Tabular Results of Models' Performance Figure 2. ROC curve for Naïve Bayes after 10-fold cross validation Accuracy Sensitivity Specificity Mean Error Kappa Statistic Neural Network Decision Tree Naïve Bayes Figure 3. ROC Curve for Decision Tree after 10-fold Cross Validation FIGURE 4. ROC CURVE FOR MULTI-LATER PERCEPTRON AFTER 10-FOLD CROSS VALIDATION

5 7 CONCLUSION The overwhelming rates of low survivability as a direct result of breast cancer diagnosis is the motivation behind this research especially with a noticed scarcity of the reports and data mining projects complementing the clinical research going in Saudi Arabia. Results from predictive models are useless without the analysis and feedback of those in the field in order to decide if the results obtained are logical and if they are new findings that are novel in the medical field. Thus, data mining and the medical domain are two integrated areas that complement each other. Although data mining is becoming a complementary application for many clinical researches in the medical and bioinformatics fields, there are still limitations that can t be ignored. Total dependence on the automation of data mining is not always feasible. Human intervention via interpretation is needed to explore the extracted knowledge. This research can be extended in different ways to increase its usefulness and effectiveness. First, an ensemble of predictive models can be implemented. This will increase the accuracy and will help in introducing novel data mining techniques. Also, the range of survivability could be expanded beyond breast cancer to include survivability rates comparisons amongst different cancer types. 8 REFERENCES [1] Bellaachia and Guven Predicting cancer survivability using data mining technique. George Washington University, Department of Computer Science. [2] Dursun, D Predicting Breast Cancer Survivability: a Comparison of Three Data Mining Methods. Artificial Intelligence in Medicine, 34 (2), [3] Scharber, Evaluation of Open Source Text Mining Tools for Cancer Surveillance: Phase I: Understanding text mining and identifying tools. NPCR-AERRO Technical Development Team. [4] Zhou, Z & Jiang, Y.(2003). Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. Information Technology in Biomedicine, 7(1), [5] Shital Shah &Andrew Kusiak, Cancer Gene Search with Data Mining and Genetic Algorithms. Computers in Biology and Medicine, 37 (2007) [6] Jonsdottir,T,et al,2008. The Feasibility of Constructing a Predictive Outcome Model for Breast Cancer using the Tools of Data Mining. Expert Systems with Applications, 34(2008), G. [7] Quinlan, J,R.,1996. Learning Decision Tree Classifiers. ACM Comput. Surv. 28(1): (1996)

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