A BIOINFORMATIC TOOL FOR BREAST CANCER PREDICTION USING MACHINE LEARNING TECHNIQUES

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1 International Journal of Computer Engineering and Applications, Volume VII, Issue III, September 14 A BIOINFORMATIC TOOL FOR BREAST CANCER PREDICTION USING MACHINE LEARNING TECHNIQUES Megha Rathi 1, Vikas Pareek 2 1 Department of Computer Engineering, JIIT Noida, India 2 Department of Computer Engineering Banasthali University, Rajasthan, India ABSTRACT: In this paper we have presented a tool for prediction of type of Breast Cancer with the help of Machine Learning algorithms. For this purpose, a software tool is developed which helps oncologist in diagnosing the cancer type with in no time and then helps oncologist in decision making in treatment method. The main reason behind developing this tool is that the number of bioinformatics tool for prediction of target class is very scarce and rare. Moreover this tool could be used by oncologist for prediction of the type of Cancer of a breast Cancer patient and thus reducing the cost of treatment for patients. In this paper we have proposed a Bioinformatics tool for prediction of type of Cancer of breast cancer patients. We have implemented four different classifiers that are SVM, FT, END Meta, Naïve Bayes for comparison on different parameters that are accuracy, root mean squared error mean absolute error, Kappa statistics as well as prediction of the type of cancer of a particular breast cancer patient on inputting the value of the attributes. The data we used is from UCI ML repository i.e. Wisconsin Breast Cancer [1] consisting of 699 tuples. The data has 9 attributes and a target class with Benign or Malignant as target variables. This tool could serve as a boon because it could help oncologists to determine the type of breast cancer within no time. It is found in the proposed study that our classifiers predict with more accuracy than WEKA classifiers. Keywords: Breast Cancer, Machine Learning, Pre Processing, UCI ML Repository [1] INTRODUCTION Breast Cancer is one of the most life threatening disease of the world. During the past few decades, breast cancer has emerged to be one of the top cancer killers amongst women worldwide [2]. In the year 2009 it has been investigated that, one million new cases were diagnosed and more than five hundred thousand lives were claimed by breast cancer globally [2].According to [3] in the year 2010 one and a half million new cases was diagnosed. Despite the high incidence rates of occurrence of breast cancer in women, the one diagnosed with breast cancer are still alive 5 years after their diagnosis, which is due to detection and treatment [3]. Thus this shows that early prediction of Breast Cancer disease and its treatment at an early stage can reduce the mortality rate globally. Thus it is very essential to diagnose this life threatening disease at an early stage of life. One of the major clinical problems of this disease is the prediction of type of breast cancer. Thus the primary time goes in knowing about the type of breast cancer during which 69

2 Bioinformatic Tool For The Prediction Of Breast Cancer Using Machine Learning Techniques many of the states of breast cancer gets passed. Thus this tool could helps to predict type of breast cancer of patients, hence saving valuable time which could help in controlling mortality rate to a large extent. Thus prediction of type of breast Cancer more accurately and precisely would help oncologists to diagnose and in the treatment of breast cancer. Thus the help in controlling the mortality rate is the main reason or motivation behind the developing such a tool which could predict type of cancer of a breast cancer patient. This would further help in reducing the colossal cost of treatment for the patients and would thus help oncologists to make more accurate decisions in diagnosis and treatment of the patient s disease. Moreover, [4] expressed that in situations where experienced oncologists are not available, predictive models created with data mining techniques can be used to support physicians in decision making with acceptable accuracy. In this study we have proposed a Bioinformatics tool which could help in prediction of the type of breast cancer of a patient by using different machine learning algorithms that are SVM, End Meta, FT, and Naïve Bayes by using Weka API. Also we compared these algorithms and came to a conclusion about the best classifier on the parameters of accuracy, precision and Kappa statistics. For this purpose we experiment our Machine Learning algorithm on Breast Cancer Dataset taken from UCI ML Repository. Inputted data has some attributes which are helpful in determining the Cancer type as Benign or Malignant. Attributes are depicted in the given table. Table 1 reflects the data set attributes. Clump_Thickness Cell_Size_Uniformity Cell_Shape_Uniformity Marginal_Adhesion Single_Epi_Cell_Size Bare_Nuclei Bland_Chromatin Normal_Nucleoli Mitoses Class [2] BACKGROUND STUDY Table: 1. Dataset Attributes [2.1] MACHINE LEARNING APPROACH {bening,malignant} Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviours based on empirical data, such as from sensor data or databases. A learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying probability distribution. Data can be seen as examples that illustrate relations between observed variables. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in 70

3 International Journal of Computer Engineering and Applications, Volume VII, Issue III, September 14 the fact that the set of all possible behaviours given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases [5]. Machine learning includes several steps. The steps are as follows [6]:- Data Collection: In machine learning process data is being gathered and is used a s training set for modeling of our model to predict target class or variable. The data consists of labels and attributes. In this model we downloaded Wisconsin breast cancer data from UCI in CSV format. The data consists of 699 tuples with 9 attributes and 1 target variable as Benign or Malignant. Data Representation and Data Cleaning: The purpose of data cleaning is to remove noise and irrelevant information out of the dataset. We used Weka API filter command for cleaning our data. This process includes how the data are encoded into features when presented to learning algorithm. This process is very important because the representation of data tell us which algorithm will suit better on the data. The choice of representation may impact the choice of learning algorithm. The Feature selection algorithm is used in SVM for data representation. [2.2] MODELING We used different classifiers in case of modelling that are: [2.2.1] NAÏVE BAYES The Naïve Bayes technique depends on the famous Bayesian approach following a simple, clear and fast classifier [7]. It has been called Naïve due to the fact that it assumes mutually independent attributes. In practice, this is almost never true but is achievable by pre-processing the data to remove the dependent categories [7]. This method has been used in many areas to represent, utilize, and learn the probabilistic knowledge and significant results have been achieved in machine learning [7]. [2.2.2] SUPPORT VECTOR MACHINE A Support Vector Machine (SVM) performs classification by constructing an N- dimensional hyper plane that optimally separates the data into two categories [8]. SVMs are set of related supervised learning methods used for classification and regression [9]. They belong to a family of generalized linear classification. A special property of SVM is, SVM simultaneously minimize the empirical classification error and maximize the geometric margin. So SVM called Maximum Margin Classifiers. SVM is based on the Structural risk Minimization (SRM). SVM map input vector to a higher dimensional space where a maximal separating hyper plane is constructed. Two parallel hyper planes are constructed on each side of the hyper plane that separate the data. The separating hyper plane is the hyper plane that maximize the distance between the two 71

4 Bioinformatic Tool For The Prediction Of Breast Cancer Using Machine Learning Techniques parallel hyper planes. An assumption is made that the larger the margin or distance between these parallel hyper planes the better the generalization error of the classifier will be [9]. Thus we implemented the above algorithm for prediction of type of Breast Cancer patients on Wisconsin breast cancer data having 699 tuples by using Weka API. [2.2.3] END META Meta Classifier takes a search algorithm and evaluates next to the base classifier.this makes the attribute selection process completely transparent and the base classifier receives only the reduced dataset. Meta classifiers by default just to return the capabilities of their base classifiers- In case of descendants of, and overall the capability of the base classifiers is returned [10]. Due to this behaviour the capabilities depend only on the currently configured base classifier. [2.2.4] FUNCTION TREE According to [11] in the theory of complex systems, a function tree is a diagram showing the dependencies between the functions of a system. It breaks a problem (or its solution) down into simpler parts. When used in computer programming, a function tree visualizes which function calls another. Functional trees have been proposed and used by several researchers both in machine learning and statistic communities [12].Thus works are oriented towards single algorithms, discussing different methods to generate the same kind of decision models. [2.3] ESTIMATION AND VALIDATION We implemented four algorithms that are SVM, End Meta, Naïve bayes, FT. These algorithms were evaluated and compared to each other on the basis of Kappa Statistics, Accuracy, and Mean Absolute Error. [2.3.1] KAPPA STATISTICS When two binary variables are attempts by two individuals to measure the same thing, you can use Cohen's Kappa (often simply called Kappa) as a measure of agreement between the two individuals [13]. Kappa measures the percentage of data values in the main diagonal of the table and then adjusts these values for the amount of agreement that could be expected due to chance alone. Table 2 reflects the Metrics for Performance Evaluation. 1. TP (true positive) 2. FN (false negative) 3. FP (false positive) Predicted Class Actual Class=Yes Class=No Class Class=Yes a: TP b: FN Class=No c: FP d: TN Table: 2. Performance Evaluation Metrics 72

5 International Journal of Computer Engineering and Applications, Volume VII, Issue III, September TN (true negative) To compute Kappa, you first need to calculate the observed level of agreement 5. Pr(α) = (a+ d)/(a+b+c+d) (1) This value needs to be compared to the value that you would expect if the two raters were totally independent Pr (e)=(a+c)/(a+b+c+d)*(a+b)/(a+b+c+d)+(b+d)(a+b+c+d) * (c+d)/(a+b+c+d) (2) The value of Kappa is defined as 6. K = Pr(α)-Pr(e)/1-Pr(e) (3) The numerator represents the discrepancy between the observed probability of success and the probability of success under the assumption of an extremely bad case. Independence implies that pair of raters agrees raters agrees about as often as two pairs of people who effectively flip coins to make their ratings. The higher the value of kappa statistics the more is the suitability of particular algorithm to the data. [2.3.2] ACCURACY The accuracy of a measurement system is the degree of closeness of measurements of a quantity to that quantity's actual (true) value. The formula of accuracy from the above followed confusion matrix is:- Accuracy = (a+d)/(a+b+c+d) (4) Thus higher the accuracy of an algorithm on a particular data better is the algorithm suited on that database. [2.3.3] MEAN ABSOLUTE ERROR Mean absolute error (MAE) is a quantity used to measure how close is predictions are to the eventual outcomes [14]. The mean absolute error is given by (5) As the name suggests, the mean absolute error is an average of the absolute errors e i = f i y i, where f i is the prediction and y i the true value. Note that alternative formulations may include relative frequencies as weight factors. The mean absolute error is one of a number of ways of comparing predictions with their eventual outcomes. Thus lesser the value of mean absolute error high is the suitability of that algorithm to the particular dataset. 73

6 Bioinformatic Tool For The Prediction Of Breast Cancer Using Machine Learning Techniques [2.3.4] ROOT MEAN SQUARE ERROR Root mean-square deviation (RMSD) or root-mean-square eror (RMSE) is a frequently used measure of the differences between values predicted by a model or an estimator and the values actually observed from the thing being modelled or - estimated. The RMSD of an estimator with respect to the estimated parameter θ is defined as the square root of the mean square error [15]: RMSD(θ ) = = ) (6) For an unbiased estimator, the RMSD is the square root of the the standard error. variance, known as [2.4] BREAST CANCER OVERVIEW Globally it is found that Breast Cancer is the life threatening disease in women and leading cause of mortality among women. Breast cancer in actual is a malignant tumor that initiates in the cells of the breast [16]. A woman s breast is comprised of glands that make breast milk, ducts, fatty and connective tissue, blood vessels, and lymph vessels. In most of the cases breast cancers begin in the cells that line the ducts and is known as Ductal cancer, a kind of breast cancer while some begin in the lobules and is known as Lobular cancer and in small proportion cancer begin in other tissues [16]. Most lumps are not malignant that is they are benign. This means that they are not cancer. Actually, Benign Breast tumors are abnormal growth, but they do not expand to other part or outside and they are not life threatening. But, it is also found that some breast lumps can increase a woman s risk of getting this life threatening disease well known as Breast Cancer. [2.4.1] RISK FACTORS FOR BREAST CANCER Mutation in DNA can lead normal breast cells to become cancer. DNA is the chemical reaction in our cells that make up our genes- the instructions of how our cells work. Some changes in the DNA sequence can enhance the risk of getting cancer. For Example, BRCA1 and BRCA2 are tumor suppressor genes. When they get mutate they no longer cause cells to die at right time and hence cancer is more likely to develop [16]. Following are listed some risk factors that you cannot change: 1. Gender: Being a female is the highest priority risk for getting Breast cancer, while even men also get the disease but the ratio is quiet low. 2. Age: Chance of getting breast cancer increase as your age increases that is it goes up as a woman gets older. It has been found that about 2 out of 3 women suffering with breast cancer are 55 or older [16]. 3. Genetic risk factor: It has been found that about 5% - 10 % of breast cancer is because of inherited mutations in genes. Most common gene changes are those of BRCA1 and BRCA2 74

7 International Journal of Computer Engineering and Applications, Volume VII, Issue III, September 14 [16]. It is also found that woman with these gene mutation have up to 80% chance of getting breast cancer during their lifetimes. 4. Family History: It is also one of the leading risk factor of Breast cancer. This disease is higher among women whose close blood relative is suffering from the same disease. 5. Race: According to research [16] it has been found that overall white woman is more likely to get breast cancer that African-American woman. 6. Personal History of Breast Cancer: Woman suffering from breast cancer in one breast is having higher chance of getting a new breast cancer in other breast or other part of the same breast. 7. Dense Breast tissue: Dense breast tissue means that more is gland tissue and less is fatty tissue. A woman with dense breast tissue is most prone to this disease. [2.4.2] SIGN AND SYMPTOMS OF BREAST CANCER Following are some signs and symptoms found according to the research [16]. 1. Swelling of all or part of the breast. 2. Skin irritation or dimpling 3. Breast pain 4. Nipple pain or the nipple turning inward 5. Redness, scaliness, or thickening of the nipple or breast skin. 6. A nipple discharge other than breast milk. [3] EXPERIMENTAL STUDY We have implemented the tool known as Breast Cancer Prediction Tool with the help of Java net beans IDE 6.9 and implemented four different prediction algorithms that are:- End Meta, FT,SVM, Naïve Bayes.Sequence of working of our Tool is: 1. Choose a Training File. 2. Choose an Algorithm from the dropdown list. 3. Input the value 4. Predict the class attribute. 5. Predict the Accuracy and Performance of the particular algorithm on the inputted dataset. Main advantage of this tool is that it can take input in any format in contrast with the WEKA tool which can take input in two formats that is ARFF and CSV. We will make our tool independent of data format it enhances the flexibility of our tool. The tool operation is depicted with the help of given figures as shown: 75

8 Bioinformatic Tool For The Prediction Of Breast Cancer Using Machine Learning Techniques Figure: 1. Input Data File Selection As shown in Fig.1 the corresponding Breast Cancer file is selected from the database. The input file has 699 valid instances. The data is taken from UCI ML Repository. The given tool has flexibility to select input data in any format that is in contrast with WEKA tool in which you will take input in two formats only that is in ARFF and CSV. While in our case we will make our tool independent of data format. And it s a great advantage over WEKA in which input is restricted to some standard format. Figure: 2. Application Interface showing Algorithm selection As Depicted in the above fig. 2 we have implemented four different prediction algorithms and investigated this entire algorithm on the inputted data set to check the accuracy and predictive capability of these algorithms 76

9 International Journal of Computer Engineering and Applications, Volume VII, Issue III, September 14 Figure: 3. Application Interface showing final Results After experimenting the entire given algorithm on the data set, the result of corresponding Predictive algorithm is depicted in fig.3.the given tool has advantage of inputting the data manually and check or predict the cancer type for the given inputted values even for the single entry or you can input the complete data file and check the result. The tool has provided complete flexibility and provides result with greater accuracy. [4] RESULTS AND DISCUSSIONS Table 3 shows the result from Weka on 10 cross fold validation. Algorithm Accuracy Kappa Statistics Root mean Square Error Mean absolute Error FT Naïve Bayes End Meta SVM Table: 3. Result from Weka Table 4 shows the result from our implemented Breast Cancer Prediction tool. RMSE Root Mean Square Error 77

10 Bioinformatic Tool For The Prediction Of Breast Cancer Using Machine Learning Techniques Algorithm Accuracy Kappa Statistics RMSE Mean absolute error FT Naïve Bayes End Meta SVM Table: 4. Result from our study In this work, a software tool is developed to assist doctors in breast cancer type prediction applying machine learning techniques. This tool is made for predicting breast cancer that suggests doctors to use that result for treatment options. Total 699 valid instances from Wisconsin Breast cancer data are processed with our tool after algorithm assessment SVM shows the best relevant output for breast cancer prediction.the main difficulties of this work are the limited amount of Breast Cancer Data and missing data of some attributes. According to the results depicted in the fig.4 SVM achieve higher accuracy(97.71%) on the given data set then Naïve Bayes achieve second highest accuracy(97.56%),ft achieve accuracy(96.99%),amongst all implemented algorithm End Meta shows worst accuracy(94.56%) [5] CONCLUSION The tool is developed specially for oncologist for predicting cancer type with in no time and thus helps in decision making for the treatment method.this paper implemented using machine learning techniques can be helpful in diagnosing the cancer type and to assist oncologist for decision support. For this purpose our software is developed to help oncologist in diagnosing the cancer type and suggesting the treatment method about breast cancer patients. We have implemented four algorithms End Meta, Naïve Bayes, SVM, FT and it has been found that SVM algorithm shows best accuracy performance compared to each other. When the amount of data is increased the results will be more sensible. Also this study can be applied to different cancer types as well as to different disease. Also it is found that our implemented classifiers provide more accuracy than WEKA classifiers. In conclusion this study shows that machine learning techniques can be a useful tool for medical diagnosis and applications particularly at treatment decision statement. This tool helps oncologist to decide in a short time whether the cancer is benign or malignant and based on that helps at treatment decision step. 78

11 International Journal of Computer Engineering and Applications, Volume VII, Issue III, September 14 REFERENCES [1] Data set Repository ( /ml /datasets/ Breast+Cancer+Wisconsin+ %28Original%29) [2] Breast cancer awareness ( www. notouchbreastscan.com /awarenessglobaldisease.html ) [3] Bresat cancer statistics ( www. world widebreastcancer.com / learn/ breastcancer-statistics-worldwide) [4] Amir E, Evans DG, Shenton A, Lalloo F, Moran A, Boggis C, WilsonM, Howell A. (2003): Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme. J Med Genet 40,pp: [5] Machine Learning Overview ( en.wikipedia. org/ wiki/machine_learning). [6] Machine Learning ( [7] Ian H. Witten and Eibe Frank. Data Mining:Practical machine learning tools and techniques,2nd Edition. San Fransisco:Morgan Kaufmann;2005. [8] Support Vector Machine ( [9] V. Vapnik. The Nature of Statistical Learning Theory. NY: Springer-Verlag [10] End Meta Algorithm (http//weka.wikispaces.com) [11] Function Trees ( /wiki/function_tree). [12] Function Trees ( [13] KappaStatistics ( [14] Mean Absolute Error ( error) [15] Root Mean Square Error ( [16] American cancer society. Breast cancer facts and figures ( [17] Ryu, Y. U., Chandrasekaran, R., and Jacob, V. S., Breast cancer prediction using the isotonic separation technique. Eur. J. Oper. Res. 181: , 2007 [18] Overview: Breast Cancer ( [19] Breast Cancer ( [20] Breast Cancer Treatment ( [21] Data mining ( sid87_gci) [22] Tang, Z., and MacLennan, J., Data Mining with Sql Server Wiley, [23] Delen, D., Walker, G., and Kadam, A., Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34: , [24] Witten, I. H., and Frank, E., Data mining: practical machine learning tools and techniques. Morgan Kaufmann Academic Press, America, p. 525, [25] Weka( [26] Alireza Osareh, Bita Shadgar, Machine Learning Techniques to diagnose Breast Cancer. IEEE, 2009 [27] Subbalakshmi G., Ramesh K., Chinna Rao M., Decision support in Heart Prediction System using Naïve Bayes, IJCSE,

12 Bioinformatic Tool For The Prediction Of Breast Cancer Using Machine Learning Techniques [28] Abdulkadir Cakir, Burcin Demirel, A Software Tool for determination of Breast Cancer Treatment methods using Data Mining Approach.Springer,2010. [29] Shelly Gupta, Dharminder Kumar, Anand Sharma, Data Mining Classification Techniques applied for Breast Cancer Diagnosis and Prognosis.IJCSE, [30] R. Setiono, Generating concise and accurate classification rules for breast cancer diagnosis Artificial Intelligence in Medicine.vol.18,pp ,2000 [31] Wu, R., Peters, W., Morgan, M.W. The next generation clinical Decision support: Linking Evidence to Best Practice, Journal Healthcare Information Management. 16(4), 50-55,