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Early Detection of Brain Cancer in Obese and Non- Obese Patients by using Data Mining Techniques
Objectives: To detect brain cancer in obese and non-obese patients by applying datamining and classification techniques. These techniques will be beneficial for both patients and doctors. Methods/Statistical Analysis: Data mining is a broader term for a variety of data analysis techniques. These techniques will be applied to extract meaningful knowledge from a large and noisy database. These techniques have special ability to adapt to the local characteristics of the data. Different data mining techniques like data preprocessing, decision tree C5.0 algorithm are used for discovering useful and meaningful information from medical data, like for brain cancer prediction in obese and non-obese patients. Findings: From the last few years, the Data of Medical Science become vast rapidly. There is a need to extract meaningful knowledge from the large data set by applying data mining techniques. Cancer is one of the major diseases and brain cancer is one of the major types of cancer which become major cause of death in men as well as in women. Brain cancer is also known as brain tumor and it is becoming the cause of death due to brain cancer in Pakistan and Worldwide. Obesity is also one of the major factors of brain tumor. Brain cancer starts when cells of brain become abnormal and grow out of control. Excess body fat changes the level of hormones. These hormones release chemicals in immune cells that cause long lasting inflammation and this inflammation in cells can raise the risk of cancer. In this paper, we shall discover the relationship of obesity with brain cancer and how obesity is also a major cause of brain tumor. Application/Improvements: The results which we concluded from this method can be used as supporting or assistant tool for neuro-oncologist for identification and diagnosis of brain tumor.
Carcinogens, Data Mining (DM), Meningioma, Neuro-oncologist, Obesity, Pre-Processing
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