Data Mining Techniques In Computer Aided Cancer Diagnosis

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Data Mining Techniques In Computer Aided Cancer Diagnosis

Data mining techniques in computer aided cancer diagnosis

Data mining in cancer diagnosis Bio medical data mining happens to be a long standing problem in scientific research where scientists are looking for innovative ideas methods to mine bio-medical data. Data mining in CAD(computer aided diagnosis) helps doctors to make optimal decisions quickly and accurately.

Data mining techniques allow the doctors to quickly categorize the difference between malignant and benign tumors. Key factor analysis is done to find the difference between benign and tumor cells. Decision trees may be used for clustering, classification, prediction or estimation. One of the useful applications in medical sciences is in management of leukemia as it accounts for about 33% of pediatric malignancies.

ALL Child acute lymphatic leukemia( also called acute lymphatic leukemia or ALL) is a cancer of blood and bone marrow. Its most common in children and gets worse if not treated in early stages.

Key data mining techniques used in ALL diagnosis are: Neural networks Decision trees Cluster detection Genetic algorithms

Benefits of using data mining techniques in CAD Data mining techniques help physicians in a variety of ways: ,[object Object]

Combine information from multiple sources

Provide support for differential diagnosis

Providing patient-specific prognosis.,[object Object]

Data warehousing, data mining, and decision support systems to reduce national cancer burden An information system is presented to deliver the technology and knowledge that users need readily to: ,[object Object]

Detect cancer patterns in general and special populations

Formulate models that explains the patterns

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Healthcare facilities have at their disposal vast amounts of cancer patientsÃ¢â‚¬â„¢ data. Medical practitioners require more efficient techniques to extract relevant knowledge from this data for accurate decision-making. However the challenge is how to extract and act upon it in a timely manner. If well engineered, the huge data can aid in developing expert systems for decision support that can assist physicians in diagnosing and predicting some debilitating life threatening diseases such as cancer. Expert systems for decision support can reduce the cost, the waiting time, and liberate medical practitioners for more research, as well as reduce errors and mistakes that can be made by humans due to fatigue and tiredness. The process of utilizing health data effectively however, involves many challenges such as the problem of missing feature values, the curse of dimensionality due to a large number of attributes, and the course of actions to determine the features that can lead to more accurate diagnosis. Effective data mining tools can assist in early detection of diseases such as cancer. In This paper, we propose a new approach called IGANFIS. This approach optimally minimizes the number of features using the information gain (IG) algorithm which is usually used in text categorization to select the quality of text. The IG will be used for selecting the quality of cancer features by virtue of reducing them in number. The reduced number quality features dataset will then be applied to the Adaptive Neuro Fuzzy Inference System (ANFIS) to train and test the proposed approach. ANFIS method of training is ideally the hybrid learning algorithm which uses the gradient descent method and Least Square Estimate (LSE) for computing the error measure for each training pair. Each cycle of the ANFIS hybrid learning consists of a forward pass to present the input vector calculating the node outputs layer by layer repeating the process for all data and a backward pass using the steepest descent algorithm to update parameters, a process called back propagation.

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Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.

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Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.

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According to World Health Organization (WHO), breast cancer is the top cancer in women both in the developed and the developing world. Increased life expectancy, urbanization and adoption of western lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer outcome and survival is very crucial. In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of the other patients are realized through seven different algorithms and the accuracies of those have been given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr. William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process, RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.

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Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of machine learning.

Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...

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I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.

Twenty Years of Whole Slide Imaging - the Coming Phase Change

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The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.

GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...

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The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.

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The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.

GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...

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Damo Consulting Inc.

Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance. This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed the research papers which mainly concentrate on predicting various disease

HEALTH PREDICTION ANALYSIS USING DATA MINING

HEALTH PREDICTION ANALYSIS USING DATA MINING

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Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics. M. Najela Fathin | Dr. S. Shajun Nisha | Dr. M. Mohamed Sathik"Comparative Study on Cancer Images using Watershed Transformation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12767.pdf http://www.ijtsrd.com/computer-science/other/12767/comparative-study-on-cancer-images-using-watershed-transformation/m-najela-fathin

Comparative Study on Cancer Images using Watershed Transformation

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Medical science journals have evolved into essential tools for advancing healthcare by disseminating research findings, promoting evidence-based practices, and fostering collaboration. Their historical significance, role in evidence-based medicine, and adaptability to the digital age make them indispensable in the quest for improved healthcare outcomes. As they continue to evolve, medical science journals will play a vital role in shaping the future of medicine and healthcare worldwide.

The Evolution and Impact of Medical Science Journals in Advancing Healthcare

The Evolution and Impact of Medical Science Journals in Advancing Healthcare

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BREAST TUMOR DETECTION USING EFFICIENT MACHINE LEARNING AND DEEP LEARNING TEC...

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Comparative Study on Cancer Images using Watershed Transformation

Comparative Study on Cancer Images using Watershed Transformation

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The Evolution and Impact of Medical Science Journals in Advancing Healthcare

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1. Data mining techniques in computer aided cancer diagnosis

2. Data mining in cancer diagnosis Bio medical data mining happens to be a long standing problem in scientific research where scientists are looking for innovative ideas methods to mine bio-medical data. Data mining in CAD(computer aided diagnosis) helps doctors to make optimal decisions quickly and accurately.

3. Data mining techniques allow the doctors to quickly categorize the difference between malignant and benign tumors. Key factor analysis is done to find the difference between benign and tumor cells. Decision trees may be used for clustering, classification, prediction or estimation. One of the useful applications in medical sciences is in management of leukemia as it accounts for about 33% of pediatric malignancies.

4. ALL Child acute lymphatic leukemia( also called acute lymphatic leukemia or ALL) is a cancer of blood and bone marrow. Its most common in children and gets worse if not treated in early stages.

5. Key data mining techniques used in ALL diagnosis are: Neural networks Decision trees Cluster detection Genetic algorithms

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7. Combine information from multiple sources

8. Provide support for differential diagnosis

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11. Detect cancer patterns in general and special populations

12. Formulate models that explains the patterns

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14. New image mining techniques for breast cancer detection The method uses an overlapped technique to precisely detect the presence of breast cancer. Statistical features are extracted from mammograms and used for decision tree induction in order to learn the knowledge for computer assisted image analysis.

15. The application of data mining will help to get some additional knowledge about specific features of different classes and the in which they are expressed in the image (can help to find some inherent non-evident links between classes and their imaging in the picture) It can help to get some non-trivial conclusions and predictions can be made on the base of image analysis. The basis for this study is sufficiently a large database with images and expert descriptions.

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17. A data viewer that provides a series of reports.

18. A map viewer that allows users to project compounds onto color maps for rapid visual analysis.

19. Links to external publications databases for related articles of interests.