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Data mining as a methodology for extracting hidden knowledge from breast cancer patient’s

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  1. Related work
  2. Proposed methods
  3. The data we use
  4. Experimental results
  5. Future work

Modern monitoring devices among other data collection devices have helped health care organization reduce their cost of collecting and storing data. Specialized tools that come with such equipment have made the entire data collection and storage process more effective thereby easing management's decision-making processes. Large medical data amounts exists in Jordan that require analysis to end up being useful.

[...] Most of these reported cases were from Amman, while most of the breast cancer patients were found to be non-smokers. Besides, most cases belonged to topographies and 504. On the other hand, the breast cancer patients' morphology was 8500. Again, the summery stage 3 was for clusters 1 and stage 5 for cluster 3 and then stage 7 for cluster 4. Mostly, alive patients were studied as opposed to deceased patients. Conclusions All that is contained in this study has presented some definitions of basic notions in the KDD field. [...]

[...] The Male Cancer Data Coordination with its trained health workers who are drawn from patient files then filed in the standard form are send straight to Jordan Cancer Registry where data is extracted including personal information as well as demographic information, diagnosis and tumor details. For the proposed method, statistical analysis is critical. In this case, the information about the tumor from specific examinations have to be gathered through staging techniques in order to establish the way the cancer is widespread. The information has to be combined through stage grouping to establish the stage of the disease. The disease could fall between stage 0 and stage IV. It would also be important to consider the aspects of knowledge discovery and data mining processes. [...]

[...] Mining breast cancer genetic data. Natural Computation (ICNC), Ninth International Conference, IEEE, 1047-1051. May, R. J., Maier, H. R., & Dandy, G. C. (2010). Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Netw, 283-294. doi: 10.1016 /j.neunet S0893-6080(09)00294-9 [pii] Meshram II, Hiwarkar PA, Kulkarni PN (2009). Reproductive Risk Factors for Breast Cancer: A Case Control Study. Online Journal of Health and Allied Sciences. Volume Issue 3. Moftah, H. M., Azar, A. T., Al-Shammari, E. T., Ghali, N. [...]

[...] In Proceedings of the tenth international conference on Information and knowledge management, ACM, 81-88. Rokach, L., & Maimon, O. (2005). Clustering methods: Springer US. Sandhya, G., Gowda, S., Swamy, L. N., Raju, G. T., & Vasumathi, D. (2013). Automated detection of cancer tissues in mammograms using advanced Means clustering with homomorphic filtering . Circuits, Controls and Communications (CCUBE), International conference IEEE, 1-4. Sharma, R., Alam, M. A., & Rani, A. (2012). K-Means Clustering in Spatial Data Mining using Weka Interface. [...]

[...] Most breast cancer patients were found to be non- smokers. Besides, topography 509 (Breast, NOS) accounted for most of the cases. Breast cancer patients' morphology was 8500. Most of the patients studied were typically alive. Regarding cluster 2 results, the cluster utilized of data. In the case of age, category G was represented by 51%. Fig.20: Representation of the age percentage in C2. In marriage status, married women accounted for for married women. Fig.21: Representation of the marital status percentage in C2. [...]

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