MINING LUNG CANCER DATA AND OTHER DISEASES DATA USING DATA MINING TECHNIQUES

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MINING LUNG CANCER DATA AND OTHER DISEASES DATA USING DATA MINING TECHNIQUES Powered By Docstoc
					  International Journal of JOURNAL OF COMPUTER (IJCET), ISSN 0976-
 INTERNATIONALComputer Engineering and Technology ENGINEERING
  6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME
                           & TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)                                                     IJCET
Volume 4, Issue 2, March – April (2013), pp. 508-516
© IAEME: www.iaeme.com/ijcet.asp
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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.

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        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.
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         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
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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

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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 Ant-
Miner 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.
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         In 2011, Mete ÇEL K 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             Disease Name           Accuracy

Hnin Wint Khaing eta      K-Means Based MAFIA               Heart Disease           74%
al. [23]                                                      Prediction
Hnin Wint Khaing eta      K-mean based MAFIA with         for Heart disease         85 %
al. [23]                  ID3                                 Prediction
Shyi-Ching Liang et al.   Ant Colony Optimization and      Breast Cancer            70.33
[28]                      Classification Rule Problem
Mete ÇEL K[29]                    ABC-Miner                Breast Cancer      Standard Deviation
                                                                                   of 0.082

Yao Liu et al. [26]       Mining Cancer data with          Lung Cancer             68.33
                          Discrete Particle Swarm                               (DPSO (new))
                          Optimization and Rule Pruning
Yao Liu et al. [26]       Mining Cancer data with          Lung Cancer              64.44
                          Discrete Particle Swarm                                (PSO (new))
                          Optimization and Rule Pruning




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 K-
        Means.

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