Improves Treatment Programs of Lung Cancer Using Data Mining Techniques (original) (raw)
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Using some data mining techniques for early diagnosis of lung cancer
2011
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. Detection of lung cancer in its early stage is the key of its cure. 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 analyzing patient's records aiming at the discovery of new knowledge potentially useful for medical decision making. Firstly we will use some processes are essential to the task of medical image mining, Data Preprocessing, Feature Extraction and Rule Generation. The methods used in this paper work states, to classify the digital X-ray chest films into two categories: normal and abnormal. The normal state is the one that characterize a healthy patient. The abnormal state including the types of lung cancer; will be used as a common classification met...
Diagnostic of Lung Cancer using Data Mining Techniques
Lung cancer is the wild growth of unusual cells in the tissues of lungs usually in the cells that line the air passages. Lung cancer is the leading cause of cancer deaths in the world, among both men and women. Lung Cancer is one of the most dangerous diseases, but there is a big chance for patient to be treated if he or she is correctly diagnosed in early stage. In this paper we review several methods and techniques for diagnosis of lung cancer. Diagnostic of lung cancer on early stage is very important. In this paper we will review some data mining techniques data preprocessing, classification, neural networks, Feature Extraction and SVM to diagnose the lung cancer on early stage. These techniques are used to check the state of patient in its early stage.
Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
International Journal of Computer Science and Information Technologies, 2013
Cancer is the most important cause of death for both men and women. The early detection of cancer can be helpful in curing the disease completely. So the requirement of techniques to detect the occurrence of cancer nodule in early stage is increasing. A disease that is commonly misdiagnosed is lung cancer. Earlier diagnosis of Lung Cancer saves enormous lives, failing which may lead to other severe problems causing sudden fatal end. Its cure rate and prediction depends mainly on the early detection and diagnosis of the disease. One of the most common forms of medical malpractices globally is an error in diagnosis. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, Decision tree, Naïve Bayes and Artificial Neural Network to massive volume of healthcare data. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. For data preprocessing and effective decision making One Dependency Augmented Naïve Bayes classifier (ODANB) and naive creedal classifier 2 (NCC2) are used. This is an extension of naïve Bayes to imprecise probabilities that aims at delivering robust classifications also when dealing with small or incomplete data sets. Discovery of hidden patterns and relationships often goes unexploited. Diagnosis of Lung Cancer Disease can answer complex “what if” queries which traditional decision support systems cannot. Using generic lung cancer symptoms such as age, sex, Wheezing, Shortness of breath, Pain in shoulder, chest, arm, it can predict the likelihood of patients getting a lung cancer disease. Aim of the paper is to propose a model for early detection and correct diagnosis of the disease which will help the doctor in saving the life of the patient
A REVIEW PAPER ON DATA MINING CLASSIFICATION TECHNIQUES FOR DETECTION OF LUNG CANCER
— Cancer is the most important cause of death for both men and women. The early detection of cancer can be helpful in curing the disease completely. So the requirement of techniques to detect the occurrence of cancer nodule in early stage is increasing. A disease that is commonly misdiagnosed is lung cancer. Earlier diagnosis of Lung Cancer saves enormous lives, failing which may lead to other severe problems causing sudden fatal end. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as BFO, SVM, LDA and Neural Network to massive volume of healthcare data. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not " mined " to discover hidden information. Using generic lung cancer symptoms such as age, sex, Wheezing, Shortness of breath, Pain in shoulder, chest, arm, it can predict the likelihood of patients getting a lung cancer disease.. In this paper we present an overview of the current research being carried out using the data mining techniques to enhance the lung cancer. Aim of the paper is to propose a model for early detection and correct diagnosis of the disease which will help the doctor in saving the life of the patient.
American Scientific Research Journal for Engineering, Technology, and Sciences, 2016
The successful diagnosis of lung cancer disease in early time increases the percentage of patient survival. Effective ways for predict and treat lung cancer remain challenges due to lack of effective ways of detection the lung nodules which causes by their arbitrariness in shape, size and texture. In this paper, image processing is used for image pre-processing, image segmentation and feature extraction. Artificial neural network (ANN) have been employed to learn extracted feature for nodule detection such as shape, size, volume.While principal component analysis were employed for multivariate data processing, it used to detect the complexity of interrelationships between diverse patient, disease and treatment variables.MATLAB have been used for all procedure in processing lung image and artificial neural network for train features extracted. XLSTART software was used for principal component analysis. The lung cancer database which contains the images classify lung image into two ki...
Survey on Lung Cancer Identification using Data Mining Techniques
According to statistics, lung cancer is the leading cause of cancer related deaths compared to any other type of cancer in the world. Lung cancer is contributing about 1.3 million deaths per year globally. Further, these reports indicate that the survival rate of lung cancer is only 14 percentages but still, if defective nodules are detected at an early stage, the survival rate can be increased up to 50 percentages. Thus the early detection of lung nodules is important in the treatment of lung cancer. These research papers contribute survey of Lung cancer identification in various aspects.
Development of Computational Tool for Lung Cancer Prediction Using Data Mining
International Journal of Computer Applications Technology and Research, 2016
The requirement for computerization of detection of lung cancer disease arises ever since recent-techniques which involve manual-examination of the blood smear as the first step toward diagnosis. This is quite time-consuming, and their accurateness depends upon the ability of operator's. So, prevention of lung cancer is very essential. This paper has surveyed various techniques used by previous authors like ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.
RECOGNIZING LUNG CANCER IN IT'S PRIMARY PHASE VIA NEURAL NETWORK AND KNN ALGORITHM
Medical data mining is one of the major issues in this modern world. With the growth in population and disease, there is need to include data mining in the field of health care industry. Studies have shown that cancer is one of the widespread diseases leading to fatal death today. Among them, lung cancer accounts the most. It has been found that if the disease is being diagnosed at an early stage, the survival rate of the patient could be improved but most of the time the disease is being diagnosed at a later stage. So Early detection and prevention of lung cancer plays a very important role in reducing death caused by cancer.
Periodicals of Engineering and Natural Sciences (PEN), 2019
These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about the cancer disease detection. Developing a proposed data mining model is useful to diagnose the cancer disease once the cancer detection is accomplished using data mining for the examination and classification of machine learning supervised algorithms.
Using Image Mining Techniques For Optimizing The Treatment Methods Of Lung Cancer
2016
Lung cancer is a disease of uncontrolled cell growth in tissues of the lung. Lung cancer is the most critical reason for death, but there is a big chance for the patient to be cured if he or she is correctly diagnosed in early stage of his or her case. Medical chest images are considered the most widely used and reliable method for the detection of lung cancer, the serious mistake in some diagnosing cases giving bad results and causing the death. The Computer Aided Diagnosis systems are necessary to support the medical staff to achieve high capability and effectiveness. Clinicians could improve treatment methods by using image mining techniques. For detecting lung nodules, number of tests should be required from the patient. Automated diagnosis system for prediction of lung cancer by using image mining techniques plays an important role in time and performance which decreases mortality rate because of early detection of lung cancer. In this research, we will present an overview of s...