Mining Lung Cancer Data and Other Diseases Data Using Data Mining Techniques

International Journal of Computer Engineering and Technology ENGINEERING (IJCET), ISSN 0976INTERNATIONAL JOURNAL OF COMPUTER 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME & TECHNOLOGY (IJCET)

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MINING LUNG CANCER DATA AND OTHER DISEASES DATA USING DATA MINING TECHNIQUES: A SURVEY Parag Deoskar1, Dr. Divakar Singh2, Dr. Anju Singh3 MTech Scholar CSE Deptt. BUIT, Barkatullah University, Bhopal1 HOD of CSE Deptt. BUIT, Barkatullah University, Bhopal2 Astt. Prof. of CSE Deptt. BUIT, Barkatullah University, Bhopal3

ABSTRACT If you think about the dangerous diseases in the world then you always list Cancer as one. Lung cancer is one of the most dangerous cancer types in the world. These diseases can spread by uncontrolled cell growth in tissues of the lung. Early detection can save the life and survivability of the patients. In this paper we survey several aspects of data mining which is used for lung cancer prediction. Data mining is useful in lung cancer classification. We also survey the aspects of ant colony optimization (ACO) technique. Ant colony optimization helps in increasing or decreasing the disease prediction value. This study assorted data mining and ant colony optimization techniques for appropriate rule generation and classification, which pilot to exact cancer classification. In addition to, it provides basic framework for further improvement in medical diagnosis. Keywords: ACO, data mining, rule pruning, Pheromone 1. INTRODUCTION Lung cancer is a disease which is because of uncontrolled cell growth in tissues of the lung. If the cancer is not treated in the early stage, this growth can spread beyond the lung in a process called metastasis into nearby tissue and, eventually, into other parts of the body. Most cancers which are in the primary stage are carcinomas that derive from epithelial cells. Common causes of lung cancer are tobacco and smoke. It is the main cause of cancer death worldwide, and it is difficult to detect in its early stages because symptoms can show their properties at advanced stages sometimes in the last stager. There are several research suggest that the early detection of lung cancer will decrease the mortality rate. 508

International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME

Decision classification is the most important task for mining any data set. The problem which is classified is mainly collaborated with the assignment of an object to an object oriented parameter that is class and its parameter [1], [2]. There are several decision tasks which we observe in several fields of engineering, medical, and management related science can be considered as classification problems. Popular examples are pattern classification, speech recognition, character recognition, medical diagnosis and credit scoring. But in our case classification alone is insufficient for classifying lung cancer dataset. If we consider data mining for frequent pattern classification then it is better tool for classifying relevant data from the raw dataset. The performance of association rules is directly depend on frequent pattern mining, to balance it is the core problem of mining association rules [3]. With the developing and more detailed of the research on frequent item sets mining, it is widely used in the field of data mining, for example, mining association rule, correlation analysis, classification, clustering 4],support vector machine[5] and positive association rule classification[6]. The main aim of data mining is to extract important information from huge amount of raw data. We emphasize to mine lung cancer data to discover knowledge that is not only accurate, but also comprehensible for the lung cancer detection [7], [8], [9]. Comprehensibility is important whenever discovered knowledge will be used for supporting a human decision. After all, if discovered knowledge is not comprehensible for a user, it will not be possible to interpret and validate the knowledge. So we can say that trust in discovering rule knowledge is very important. In decision making, this can lead to incorrect decisions. We provide here an overview of medical data mining technique. The rest of this paper is arranged as follows: Section 2 introduces medical data mining; Section 3 describes about ant colony optimization; Section 4 describes about related works; section 5 discuss about the Theoretical extraction. Section 6 describes Conclusion. 2. MEDICAL DATA MINING If we study the definition of the term data mining, then we can say data mining refers to extracting or “mining” knowledge from large amounts of data or databases [10]. The process of finding useful patterns or meaning in raw data has been called KDD [11]. KDD provides a cleaning to the inconsistent data. Data Mining also provides pattern classification, visualization and rule separation. For understanding the utility of data mining then we better categorize data mining based on their function ability as below [12]: 1) Regression is a statistical methodology that is often used for numeric prediction. 2) Association returns affinities of a set of records. 3) Sequential pattern function searches for frequent subsequences in a sequence dataset, where a sequence records an ordering of events. 4) Summarization is to make compact description for a subset of data. 5) Classification maps a data item into one of the predefined classes. 6) Clustering identifies a finite set of categories to describe the data. 7) Dependency modeling describes significant dependencies between variables. 8) Change and deviation detection is to discover the most significant changes in the data by using previously measured values. 509


Medical diagnosis is very subjective because of the clinical research and personal perception of the doctors matter the diagnosis. A number of studies have shown that the diagnosis of one patient can differ significantly if the patient is examined by different doctors or even by the same doctors at various times [13]. The idea of medical data mining is to extract hidden knowledge in medical field using data mining techniques. It is possible to identify patterns even if we do not have fully understood the casual mechanisms behind those patterns. Even the patterns which are irrelevant can be discovered [14]. Clinical repositories containing large amounts of biological, clinical, and administrative data are increasingly becoming available as health care systems integrate patient information for research and utilization objectives [15]. Data mining techniques applied on these databases discover relationships and patterns which are helpful in studying the progression and the management of disease [16]. A typical clinic data mining research including following ring: structured data narrative text, hypotheses, tabulate data statistics, analysis interpretation, new knowledge more questions, outcomes observations and structured data narrative text [17]. Prediction or early diagnosis of a disease can be kinds of evaluation. About diseases like skin cancer, breast cancer or lung cancer early detection is vital because it can help in saving a patient’s life [18]. 3. ANT COLONY OPTIMIZATION The Ant Colony Optimization (ACO) algorithm is a meta-heuristic which is a grouping of distributed environment, positive feedback system, and systematic greedy approach to find an optimal solution for combinatorial optimization problems. The Ant Colony Optimization algorithm is mainly inspired by the experiments run by Goss et al. [19] which using a grouping of real ants in the real environment. They study and observe the behaviour of those real ants and suggest that the real ants were able to select the shortest path between their nest and food resource, in the existence of alternate paths between the two. The above searching for food resource is possible through an indirect communication known as stigmergy amongst the ants. When ants are travelling for the food resources, ants deposit a chemical substance, called pheromone, on the ground. When they arrive at a destination point, ants make a probability based choice, biased by the intensity of pheromone they smell. This behaviour has an autocatalytic effect because of the very fact that an ant choosing a path will increase the probability that the corresponding path will be chosen again by other ants in the next move. After finishing the search ants return back, the probability of choosing the same path is higher because of increasing pheromone quantity. So by the pheromone will be released on the chosen path, it provides the path for the ants. In short we can say that, all ants will select the shortest path. Figure 1 shows the behaviour of ants in a double bridge experiment [20]. If we analyse the case then we observed that because of the same pheromone laying the shortest path will be chosen. It will be starts with first ants which arrive at the food source are those that took the two shortest branches. After approaching the food destination these ants start their return trip, more pheromone is present on the short branch is the possibility for choosing the shortest one than the one on the Long Branch. This ant behaviour was first formulated and arranged as Ant System (AS) by Dorigo et al. [21]. Based on the AS algorithm, the Ant Colony Optimization (ACO) algorithm was proposed [22]. In ACO algorithm, the optimization problem can be expressed as an formulated graph G = (C; L), where C is the set of components of the problem, and L is the set of possible connections or transitions among 510


the elements of C. The proposed solution is represented in terms of feasible paths on the graph G, with respect to a set of given constraints and predicate. The population of ants that is also called agents collectively solves the problem under consideration using the graph representation. We assume that the ants are probably very poor of finding a solution, good quality solutions can emerge as a result of collective interaction amongst ants. Pheromone trails encode a long-term memory about the whole ant search process from the starting to the food resource destination. The value depends on the problem formulation, representation and the optimization objective which is different in case to case.

Figure 1: Double bridge experiment. (a) Ants start exploring the double bridge. (b) Eventually most of the ants choose the shortest path [20]. The algorithm presented by Dorigo et al. [22] was given below: Algorithm ACO meta heuristic(); while (termination criterion not satisfied) ant generation and activity(); pheromone evaporation(); daemon actions(); “optional” end while end Algorithm

4. RELATED WORKS In 2011, Hnin Wint Khaing et al. [23] presented an efficient approach for the prediction of heart attack risk levels from the heart disease database as presented by the authors. They proposed the algorithm in which the heart disease database is firstly clustered for creating alike element grouping using the K-means clustering algorithm. Their approach allows mastering the number of fragments through its k parameter. After that they apply mining on frequent patterns from the extracted data, which are relevant to heart disease, using the MAFIA (Maximal Frequent Item set Algorithm) algorithm. Then the learning algorithm 511


is trained with the selected significant patterns for the effective prediction of heart attack diseases. They have employed the ID3 algorithm as the training algorithm to show level of heart attack with the decision tree. According to the author results showed that the designed prediction system is capable of predicting the heart attack effectively. In 2011, Zenggui Ou et al. [24] discuss about how to use the sequential characteristic in the course of Web data mining to carry out structural transfer of semi-structured data based on time effect of data, that is the systematic structuring of Web resources data, and solve the problem which is about the effectiveness in retrieval accordingly. In 2010, Zakaria Suliman Zubi et al. [25] study that the lung cancer is a disease of uncontrolled cell growth in tissues of the lung, Lung cancer is one of the most common and deadly diseases in the world. Authors suggest that the detection of lung cancer in its early stage is the key of its cure. So in general, a measure for early stage lung cancer diagnosis mainly includes those utilizing X-ray chest films, CT, MRI, etc. Medical images mining is a promising area of computational intelligence applied to automatically analysing patient's records aiming at the discovery of new knowledge potentially useful for medical decision making. In 2011, Yao Liu et al. [26] proposed and implement a classifier using discrete particle swarm optimization (DPSO) with an additional new rule pruning procedure for detecting lung cancer and breast cancer, which are the most common cancer for men and women as per the author’s observation. According to the author experiment which shows the new pruning method further improves the classification accuracy and their approach is effective in making cancer prediction. In 2011, Chandrasekhar U et al. [27] discuss and analyses recent improvements on clustering algorithms like PP (Project Pursuit) based on the ACO algorithm for high dimensional data, recent applications of Data Clustering with ACO, application of Ant-based clustering algorithm for object finding by multiple robots in image processing field and the hybrid PSO/ACO algorithm for better optimized results. According to the author Cluster Analysis is a popular and widely used data analysis and data mining technique. The high quality and fast clustering algorithms play a vital role for users to navigate, effectively organize and structure the data. They observed that Ant Colony Optimization (ACO), a Swarm Intelligence technique, integrated with clustering algorithms, is being used by many applications for past few years. In 2011, Shyi-Ching Liang et al. [28] suggest Classification rule is the most common representation of the rule in data mining. It is based on supervised learning process which generates rules from training data set. The main goal of the classification rule mining is the prediction of the predefined class based on the group. Based on ACO algorithm, Ant-Miner solved the classification rule problem. According to the author, Ant-Miner shows good performance in many dataset. In this research paper author proposed, an extension of AntMiner is proposed to incorporate the concept of parallel processing and grouping. In this paper intercommunication is provided via pheromone among ants is a critical part in ant colony optimization’s searching mechanism. The algorithm design in such a way, with a slight modification in this part which removes the parallel searching capability. Based on Ant-Miner, they propose an extension that modifies the algorithm design to incorporate parallel processing. The pheromone trail deposited by ants during the searching procedure affected each other. With the help of pheromone, ants can have better decision making while searching. They provide a possible direction for researches toward the classification rule problem. 512


In 2011, Mete ÇELİK et al. [29] discuss about several classical and heuristic algorithms proposed to mine classification rules out of large datasets. In this research authors proposed, a new and novel heuristic classification data mining approach based on artificial bee colony algorithm (ABC) ABC-Miner. Authors proposed approach was compared with Particle Swarm Optimization (PSO) rule classification algorithm and C4.5 algorithm using benchmark datasets. The experimental results show good efficiency of the proposed method. In 2011, G. Sophia Reena et al. [30] suggest that the Cancer research is an interesting research area in the field of medicine. Authors suggest that classification is momentously necessary for cancer diagnosis and treatment. The precise prophecy of dissimilar tumor types has immense value in providing better care and toxicity minimization on the patients. Author suggest that classification of patient taster obtainable as gene expression profiles has become an issue of prevalent study in biomedical research in modern years. Formerly, cancer classification depends upon the morphological and clinical. The modern arrival of the micro array technology has permitted the concurrent observation of thousands of genes, which provoked the progress in cancer classification using gene expression data. This study hub on the broadly used assorted data mining and machine learning techniques for appropriate gene selection, which pilot to exact cancer classification. In 2013, S.Vijiyarani et al. [31] reviewed and suggest thatdData mining is defined as sifting through very large amounts of data for useful information. Some of the most important and popular data mining techniques are association rules, classification, clustering, prediction and sequential patterns. Data mining techniques are used for variety of applications. In health care industry, data mining plays an important role for predicting diseases. For detecting a disease number of tests should be required from the patient. But using data mining technique the number of test should be reduced. This reduced test plays an important role in time and performance. This technique has an advantages and disadvantages. They analyses how data mining techniques are used for predicting different types of diseases. As per our study there are several woks and algorithm is presented for efficient cancer detection. The algorithms are based on data mining, fuzzy logic, particle swarm optimization etc. Several authors categorically work on different types of cancer. After analysing those research papers we analyse that several research work are based on Lung cancer, heart Diseases and breast Cancer. Some of the authors presenting good results in the case of breast cancer and Herat diseases but fail to achieve higher accuracy in the case of Lung Cancer. In 2011 yao lio et al. [26] also proposed and implement a classifier using DPSO with new rule pruning procedure for detecting lung cancer and breast cancer from the UCI repository, which are the most common cancer for men and women. In the case of Lung Cancer they achieve the accuracy of 68.33 in the case of discrete particle swarm optimization and 64.44 in the case of particle swarm optimization. In the case of breast cancer they achieve the accuracy of 97.23in the case of discrete particle swarm optimization and 97.06 in the case of particle swarm optimization. They also provide the comparison from different related techniques like PART, SMO, Naïve Bayes, KNN and classification tree. As per our analysis the result is good in the case of breast cancer. But there is the hope in the case of lung cancer, because the prediction accuracy is not so high. Data mining and Ant colony optimization with the combined effort will produce better result by using pheromone trails, which is updated automatically on the basis of iteration and frequent pattern analysis.

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5. THEORETICAL ANALYSIS The theoretical analysis of different diseases with different data mining techniques and their accuracy of detection are shown in Table 1. Table 1: Theoretical Analysis Author

Technique Name

Hnin Wint Khaing eta al. [23] Hnin Wint Khaing eta al. [23] Shyi-Ching Liang et al. [28] Mete ÇELİK[29]

K-Means Based MAFIA

Yao Liu et al. [26]

Mining Cancer data with Discrete Particle Swarm Optimization and Rule Pruning Mining Cancer data with Discrete Particle Swarm Optimization and Rule Pruning

Yao Liu et al. [26]

K-mean based MAFIA with ID3 Ant Colony Optimization and Classification Rule Problem ABC-Miner

Disease Name

Accuracy

Heart Disease Prediction for Heart disease Prediction Breast Cancer

74% 85 % 70.33

Breast Cancer

Standard Deviation of 0.082

Lung Cancer

68.33 (DPSO (new))

Lung Cancer

64.44 (PSO (new))

6. CONCLUSION The use of data mining techniques in Lung cancer classification increases the chance of making a correct and early detection, which could prove to be vital in combating the disease. In this paper, we provide a survey on lung cancer detection. We also analyses the utility of data mining by which we can find the efficient lung cancer detection technique. After analysis we find several classifications algorithm and their result by which we can find the future insights. As the area of Lung cancer is very challenging and the researchers are continuing their research progress in efficient detection, there are lot of scope in the case of efficient detection. As per our observation there are some future suggestions which are listed below: 1) We can apply neural network and Fuzzy based technique to train cancer data set for finding better classification and accuracy. 2) We can apply optimization technique like Ant Colony Optimization to optimize the classification [33] for improving the detection. 3) Machine learning environment or Support Vector machine [32] is also an insight for better detection. 4) We can use some homogeneity based algorithm to find over fitting and overgeneralization Characteristics. It can be applied by clustering algorithm like KMeans. 514


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Mining Lung Cancer Data and Other Diseases Data Using Data Mining Techniques

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