The breast cancer is a life-threatening disease observed among females all over the world. Detection and analysis of the disease is a significant part of data mining research. Classification as an essential data mining procedure also helps in clinical diagnosis and analysis of this disease. In our study, we proposed a novel Neuro-fuzzy classification based method. We applied our method to three benchmark data sets from the UCI machine learning repository for detection of breast cancer; they were namely Wisconsin Breast Cancer (WBC), Wisconsin Diagnostic Breast Cancer (WDBC), and Mammographic Mass (MM) data sets. Our objective was to diagnose and analyze breast cancer disease with the proposed method and, therefore compare its performance with two well-known supervised classification algorithms Multilayer Perceptron and Support Vector Machine. We evaluated the performance of these classification methods in terms of different measures like Accuracy, Kappa statistic, True-Positive Rate, False-Positive Rate, Precision, Recall, and F-Measure. The proposed method had an accuracy of 99.4 % with the WBC data set, 97.7 % with the WDBC data set, and 84.4 % with the MM data set; and in every aspect, it performed better than Multilayer Perceptron and Support Vector Machine based classification models. Data mining applications can be used in medical science and the Bioinformatics research field for diagnosis of critical diseases [1, 2]. Aside from other contracting diseases which end lives, breast cancer has probably become an intensely focused subject [3] for discovering cures aside from AIDS in the present decade. Breast cancer is a type of cancer disease arising from human breast tissue cells, usually from the lobules or the inner lining... ... middle of paper ... ...ral Information Processing Systems 9, USA: MIT Press, pp.162-168, 1997. [26] J. Scott Armstrong and Fred Collopy, “Error Measures For Generalizing About Forecasting Methods: Empirical Comparisons”. International Journal of Forecasting, Vol. 8, pp. 69–80, 1992. [27] Jean Carletta, “Assessing agreement on classification tasks: The kappa statistic”. Computational Linguistics, MIT Press Cambridge, MA, USA, Vol. 22, No.2, pp. 249–254, 1996. [28] Stephen V. Stehman, “Selecting and interpreting measures of thematic classification accuracy”. Remote Sensing of Environment, Vol. 62, No.1, pp.77–89, 1997. [29] Breast Cancer Wisconsin (Original) Data Set, UCI machine learning repository, July, 1992. [30] Breast Cancer Wisconsin (Diagnostic) Data Set, UCI machine learning repository, November, 1995. [31] Mammographic Mass Data Set, UCI machine learning repository, October, 2007.
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Landscape fragmentation can be characterized as a break up of a continuous landscape into more smaller, less-connected patches by roads, clearing for agriculture, commercial and residential development, and timber harvesting. Clear-cutting can break up mature, contiguous forest until the clear-cut area has regenerated to a point that it does not act as an ecological barrier to interior species or species that rely on continuous, mature forests. Much of the work that has sought to measure landscape pattern and habitat fragmentation comes out of the disciplines of conservation biology and landscape ecology (Theobald 1998). These disciplines are founded on the premise that landscape patterns strongly influence and are influenced by ecological processes (Forman and Godron 1986).
Paternoster and Bach,. (2013, May. 28 ). In Oxford Bibliographies Online. (chap. Labeling Theory - CrLabeling Theory) Retrieved Oct. 27, 2013, from http://www.oxfordbibliographies.com/view/document/
The next model is the Quadratic Trend Model. The quadratic formula uses the least-squares method to forecast and can be written as Yi =b_0+ b_1 X_1+ b_2 X_2. In this formula the only difference is b_2 X_2 represents the estimated quadratic effect on Y. Figure 1-6 represents the comparison between the linear and quadratic
This year 203,000 women will be diagnosed with Breast Cancer, and 40,000 of them are expected to die. Breast Cancer is the second leading cause of cancer-related death among women the ages of 35-54. There are numerous ways breast cancer can be treated if found early. The key to treating breast cancer is early detection, beast self-exams, and early mammograms. One out of every eight women will get diagnosed with Breast Cancer this year; therefore, new advanced technology of the treatment of Breast Cancer is the key to life after the disease.
Conclusion: At present, as there is no single treatment known to bring a definite cure for breast cancer, one of the possible solutions for combating breast cancer is through identification of reliable biomarkers that can be effectively used for early detection, prognosis ...
Breast cancer is so prevalent in women because with the development of breasts in young girls during the ages of 11 - 14, breast...
Breast cancer is one of the most common diseases and primary cause of the huge number of deaths among women worldwide. Early detection methods and exact diagnostics are essential to the success of breast cancer treatment. However, breast cancer is a heterogeneous disease that is caused by a variety of genes. Therefore an understanding of the differences between breast cancer subtypes has become the primary avenue for treatment. In order to get a deep understanding of the various subtypes, classifying cancer patients by molecular features and phenotypic of cancer subtype will be needed. To fulfill this need, Computer Scientists and Medical Researchers in the bioinformatics field have partnered together to use a high performance data science
[5] Perveen, F., Ryota, N., Imtiaz, U., and Hossain, K. M. D., (2007). “Crop land suitability analysis using a multicriteria evaluation and GIS approach, In: 5th International Symposium on Digital Earth”, pp. 1-8, The University of California, Berkeley, USA.
GIS engineering and the accessibility of computerized information on territorial and worldwide scales empower such investigates. The satellite sensor yield used to produce a vegetation realistic. This sensor framework identifies the measures of vitality reflected from the Earth's surface crosswise over different groups of the range for surface ranges of something like 1 square kilometer.
The purpose of this paper is to explain the advances being made in technology and algorithms in helping advance the accuracy of forecasting. It will contrast the forecasting methods of several decades ago with forecasting methods in use today. In discussing how errors can accumulate over time and providing simple mathematical formulas as examples, this paper intends to show how the repetition of minor errors can affect the accuracy of weather predictions.
Rapach, D.E. and Wohar, M.E. (2006) “In-sample vs. out-of-sample tests of stock return predictability in the context of data mining”, Journal of Empirical Finance 13, pp. 231–247.
Artificial neural networks are systems implemented on computer systems as specialized hardware or sophisticated software that loosely model the learning and remembering functions of the human brain. They are an attempt to simulate the multiple layers of processing elements in the brain, called neurons. These elements are implemented in such a way so that the layers can learn from prior experience and remember their outputs. In this way, the system can learn to recognize certain patterns and situations and apply these to certain priorities and output appropriate results. These types of neural networks can be used in many important situations such as priority in an emergency room, for financial assistance, and any type of pattern recognition such as handwritten or text-to-speech recognition.
Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired.